Artificial intelligence communication assistance for editing utilizing communication profiles

ABSTRACT

In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for the editing of electronic communications utilizing user communication profiles.

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional App. Ser. No.62/541,203, filed Aug. 4, 2017, titled ARTIFICIAL INTELLIGENT ASSISTANT(APLN-0003-P01), which is incorporated by reference in its entirety.

FIELD

The present application relates to a digital assistant. In particular,the present application relates to methods and systems for providing anartificial intelligent assistant to increase the effectiveness ofcommunications.

BACKGROUND

Current digital writing assistants provide simple aids to sendingcommunications, such as illustrated in FIG. 1, where the text of anelectronically composed communication is evaluated by a ‘writingassistant’ to correct content errors, such as spelling or grammaticalerrors. However, these simple aids are very limited, generally onlyaddressing corrections to content. What is needed is an intelligentdigital assistant system that is able to incorporate knowledge andcontext surrounding the communication exchanges in order to make acommunication exchange more effective.

SUMMARY

In embodiments, a method of electronic communication assistance mayinclude receiving a partial electronic communication at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the partial electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and a second electronic identifier associated with a seconduser; extracting a communication context from the partial electroniccommunication; encoding the partial electronic communication forprocessing creating an encoded partial electronic communication;retrieving from a communication profile database a first communicationprofile for the first user using the first electronic identifierassociated with the first user, wherein the first communication profilecomprises a first user communication attribute; retrieving from thecommunication profile database a second communication profile for thesecond user using the second electronic identifier associated with thesecond user, wherein the second communication profile comprises a seconduser communication attribute; processing the encoded partial electroniccommunication with a processor to generate a compositional change forthe communication content of the partial electronic communication usingat least one of the communication context, the first user communicationattribute, or the second user communication attribute to generate thecompositional change; and generating a changed electronic communicationfrom the partial electronic communication and the compositional change.

In embodiments, a method of electronic communication assistance mayinclude receiving a partial electronic communication at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the partial electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier and a second electronicidentifier associated with a second user; retrieving a firstcommunication profile, wherein the first communication profile comprisesa first user communication attribute; retrieving a second communicationprofile, wherein the second communication profile comprises a seconduser communication attribute; processing the partial electroniccommunication with a processor to generate a compositional change forthe communication content of the partial electronic communication usingat least one of the first user communication attribute or the seconduser communication attribute to generate the compositional change; andgenerating a changed electronic communication from the partialelectronic communication and the compositional change.

In embodiments, the method may further include transmitting the changedelectronic communication to the first electronic identifier associatedwith the first user, and/or transmitting the changed electroniccommunication to the second electronic identifier associated with thesecond user. The compositional change may be derived fromrepresentations of previous content and context from a plurality of userprofiles stored in the communication profile database which are similarto at least one of the first communication profile or the secondcommunication profile. The processor may be trained on large-scale datamixed with prior communication and effective communications from theplurality of user profiles. The processor may use at least one of amachine learning model, deep learning model, or other statisticallearning algorithm for creating the compositional change. Thecompositional change may be an auto-generated textual completion; theauto-generated textual completion may be a phrasal completion, and theprocessor may generate the compositional change by optimizing generatedlanguage as determined by the processor from the second usercommunication attribute. The processor may generate the compositionalchange by replicating a communication style of the first user asdetermined by the processor from the first user communication attribute.The partial electronic communication may include a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model, deep learning model, or statistical learning algorithmto score the plurality of communication templates based at least in parton the communication content, communication context, first usercommunication attribute, or second user communication attribute,communication context, first user communication attribute, or seconduser communication attribute.

In embodiments, a method of electronic communication assistance mayinclude receiving a partial electronic communication at an artificialintelligence assistant computing facility from an electronic identifierassociated with a user, the partial electronic communication comprisinga communication content and comprising or associated with the electronicidentifier associated with the user; extracting a communication contextfrom the partial electronic communication; encoding the partialelectronic communication for processing creating an encoded partialelectronic communication; retrieving from a communication profiledatabase a communication profile for the user using the electronicidentifier associated with the user, wherein the communication profilecomprises a user communication attribute; processing the encoded partialelectronic communication with a processor to generate a compositionalchange for the communication content of the partial electroniccommunication using at least one of the communication context or theuser communication attribute; and generating a changed electroniccommunication from the partial electronic communication and thecompositional change.

In embodiments, the method may include transmitting the changedelectronic communication to the electronic identifier associated withthe user, or transmitting the changed electronic communication to asecond electronic identifier associated with a second user. Thecompositional change may be derived from representations of previouscontent and context from a plurality of user profiles stored in thecommunication profile database which are similar to the communication.The processor may be trained on large-scale data mixed with priorcommunication and effective communications from the plurality of userprofiles. The processor may use at least one of a machine learningmodel, deep learning model, or other statistical learning algorithm forcreating the compositional change. The compositional change may be anauto-generated textual completion. The auto-generated textual completionmay be a phrasal completion. The processor may generate thecompositional change by optimizing generated language of a user asdetermined by the processor from a user communication attribute, maygenerate the compositional change by replicating a communication styleof the user as determined by the processor from the user communicationattribute. The partial electronic communication may include acommunication goal, and the processor may generate the compositionalchange by optimizing for impact and effectiveness of generated languagewith respect to the communication goal. The processor may generate thecompositional change further using a communication template selectedfrom a plurality of communication templates comprising at least one ofprepared text or placeholder locations for defining structural elementsfor user completion, the processor may select the communication templateusing at least one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a method of electronic communication assistance mayinclude receiving a partial electronic communication at an artificialintelligence assistant computing facility from an electronic identifierassociated with a user, the partial electronic communication comprisinga communication content and comprising or associated with the electronicidentifier; retrieving a communication profile, wherein thecommunication profile comprises a user communication attribute;processing the partial electronic communication with a processor togenerate a compositional change for the communication content of thepartial electronic communication using the user communication attributeto generate the compositional change; and generating a changedelectronic communication from the partial electronic communication andthe compositional change.

In embodiments, the method may include transmitting the changedelectronic communication to the electronic identifier associated withthe user, or transmitting the changed electronic communication to asecond electronic identifier associated with a second user. Thecompositional change may be derived from representations of previouscontent and context from a plurality of user profiles which are similarto at least one of the communication profile. The processor may betrained on large-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles, the processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language of a user as determined by the processorfrom a user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user asdetermined by the processor from the user communication attribute. Thepartial electronic communication may include a communication goal, andthe processor may generate the compositional change by optimizing forimpact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a method of electronic communication assistance mayinclude receiving a partial electronic communication at an artificialintelligence assistant computing facility from an electronic identifierassociated with a user, the partial electronic communication comprisinga communication content and comprising or associated with the electronicidentifier associated with the user and a second electronic identifierassociated with a second user; extracting a communication context fromthe partial electronic communication; encoding the partial electroniccommunication for processing creating an encoded partial electroniccommunication; retrieving from a communication profile database acommunication profile for the second user using the second electronicidentifier associated with the second user, wherein the communicationprofile comprises a second user communication attribute; processing theencoded partial electronic communication with a processor to generate acompositional change for the communication content of the partialelectronic communication using at least one of the communication contextor the second user communication attribute to generate the compositionalchange; and generating a changed electronic communication from thepartial electronic communication and the compositional change.

In embodiments, the method may include transmitting the changedelectronic communication to the electronic identifier associated withthe user, or transmitting the changed electronic communication to asecond electronic identifier associated with a second user. Thecompositional change may be derived from representations of previouscontent and context from a plurality of user profiles stored in acommunication profile database which are similar to at least one of thecommunication profiles of the first or second user. The processor may betrained on large-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language as determined by the processor from thesecond user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user.The partial electronic communication may include a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a method of electronic communication assistance mayinclude: receiving a partial electronic communication at an artificialintelligence assistant computing facility from an electronic identifierassociated with a user, the partial electronic communication comprisinga communication content and comprising or associated with the electronicidentifier and a second electronic identifier associated with a seconduser;_retrieving a second communication profile, wherein the secondcommunication profile comprises a second user communication attribute;processing the partial electronic communication with a processor togenerate a compositional change for the communication content of thepartial electronic communication using the second user communicationattribute to generate the compositional change; and generating a changedelectronic communication from the partial electronic communication andthe compositional change.

In embodiments, the method may include transmitting the changedelectronic communication to the electronic identifier associated withthe user, or transmitting the changed electronic communication to asecond electronic identifier associated with a second user. Thecompositional change may be derived from representations of previouscontent and context from a plurality of user profiles which are similarto at least one of the communication profile. The processor may betrained on large-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language as determined by the processor from thesecond user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user asdetermined by the processor from the user communication attribute. Thepartial electronic communication may include a communication goal, andthe processor may generate the compositional change by optimizing forimpact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving a partial electroniccommunication at an artificial intelligence assistant computing facilityfrom a first electronic identifier associated with a first user, thepartial electronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user and a second electronic identifier associated with asecond user; extracting a communication context from the partialelectronic communication; encoding the partial electronic communicationfor processing creating an encoded partial electronic communication;retrieving from a communication profile database a first communicationprofile for the first user using the first electronic identifierassociated with the first user, wherein the first communication profilecomprises a first user communication attribute; retrieving from thecommunication profile database a second communication profile for thesecond user using the second electronic identifier associated with thesecond user, wherein the second communication profile comprises a seconduser communication attribute; processing the encoded partial electroniccommunication with a processor to generate a compositional change forthe communication content of the partial electronic communication usingat least one of the communication context, the first user communicationattribute, or the second user communication attribute to generate thecompositional change; and generating a changed electronic communicationfrom the partial electronic communication and the compositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to theelectronic identifier associated with the user, and/or transmitting thechanged electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles stored in the communication profile database which aresimilar to at least one of the first communication profile or the secondcommunication profile. The processor may be trained on large-scale datamixed with prior communication and effective communications from theplurality of user profiles. The processor may use at least one of amachine learning model, deep learning model, or other statisticallearning algorithm for creating the compositional change. Thecompositional change may be an auto-generated textual completion. Theauto-generated textual completion may be a phrasal completion. Theprocessor may generate the compositional change by optimizing generatedlanguage as determined by the processor from the second usercommunication attribute. The processor may generate the compositionalchange by replicating a communication style of the first user asdetermined by the processor from the first user communication attribute.The partial electronic communication may include a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a system may include: a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving a partial electroniccommunication at an artificial intelligence assistant computing facilityfrom a first electronic identifier associated with a first user, thepartial electronic communication comprising a communication content andcomprising or associated with the first electronic identifier and asecond electronic identifier associated with a second user; retrieving afirst communication profile, wherein the first communication profilecomprises a first user communication attribute; retrieving a secondcommunication profile, wherein the second communication profilecomprises a second user communication attribute; processing the partialelectronic communication with a processor to generate a compositionalchange for the communication content of the partial electroniccommunication using at least one of the first user communicationattribute or the second user communication attribute to generate thecompositional change; and generating a changed electronic communicationfrom the partial electronic communication and the compositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to the firstelectronic identifier associated with the first user, or transmittingthe changed electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles which are similar to at least one of the firstcommunication profile or the second communication profile. The processormay be trained on large-scale data mixed with prior communication andeffective communications from the plurality of user profiles. Theprocessor may use at least one of a machine learning model, deeplearning model, or other statistical learning algorithm for creating thecompositional change. The compositional change may be an auto-generatedtextual completion. The auto-generated textual completion may be aphrasal completion. The processor may generate the compositional changeby optimizing generated language as determined by the processor from thesecond user communication attribute. The processor may generate thecompositional change by replicating a communication style of the firstuser as determined by the processor from the first user communicationattribute. The partial electronic communication may include acommunication goal, and the processor may generate the compositionalchange by optimizing for impact and effectiveness of generated languagewith respect to the communication goal. The processor may generate thecompositional change further using a communication template selectedfrom a plurality of communication templates comprising at least one ofprepared text or placeholder locations for defining structural elementsfor user completion. The processor may select the communication templateusing at least one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving a partial electroniccommunication at an artificial intelligence assistant computing facilityfrom an electronic identifier associated with a user, the partialelectronic communication comprising a communication content andcomprising or associated with the electronic identifier associated withthe user; extracting a communication context from the partial electroniccommunication; encoding the partial electronic communication forprocessing creating an encoded partial electronic communication;retrieving from a communication profile database a communication profilefor the user using the electronic identifier associated with the user,wherein the communication profile comprises a user communicationattribute; processing the encoded partial electronic communication witha processor to generate a compositional change for the communicationcontent of the partial electronic communication using at least one ofthe communication context or the user communication attribute; andgenerating a changed electronic communication from the partialelectronic communication and the compositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to theelectronic identifier associated with the user, or transmitting thechanged electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles stored in the communication profile database which aresimilar to the communication profile. The processor may be trained onlarge-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language of a user as determined by the processorfrom a user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user asdetermined by the processor from the user communication attribute. Thepartial electronic communication further comprises a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving a partial electroniccommunication at an artificial intelligence assistant computing facilityfrom an electronic identifier associated with a user, the partialelectronic communication comprising a communication content andcomprising or associated with the electronic identifier; retrieving acommunication profile, wherein the communication profile comprises auser communication attribute; processing the partial electroniccommunication with a processor to generate a compositional change forthe communication content of the partial electronic communication usingthe user communication attribute to generate the compositional change;and generating a changed electronic communication from the partialelectronic communication and the compositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to theelectronic identifier associated with the user, or transmitting thechanged electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles which are similar to at least one of the communicationprofile. The processor may be trained on large-scale data mixed withprior communication and effective communications from the plurality ofuser profiles. The processor may use at least one of a machine learningmodel, deep learning model, or other statistical learning algorithm forcreating the compositional change. The compositional change may be anauto-generated textual completion. The auto-generated textual completionmay be a phrasal completion. The processor may generate thecompositional change by optimizing generated language of a user asdetermined by the processor from a user communication attribute. Theprocessor may generate the compositional change by replicating acommunication style of the user as determined by the processor from theuser communication attribute. The partial electronic communicationfurther comprises a communication goal, and the processor may generatethe compositional change by optimizing for impact and effectiveness ofgenerated language with respect to the communication goal. The processormay generate the compositional change further using a communicationtemplate selected from a plurality of communication templates comprisingat least one of prepared text or placeholder locations for definingstructural elements for user completion. The processor may select thecommunication template using at least one of a machine learning model,deep learning model, or statistical learning model to find a mosteffective communication template based at least in part on thecommunication content. The plurality of communication templates mayinclude at least one automatically generated template generated by theprocessor. The processor may select the communication template by usinga machine learning model to score the plurality of communicationtemplates based at least in part on the communication content,communication context, first user communication attribute, or seconduser communication attribute.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving a partial electroniccommunication at an artificial intelligence assistant computing facilityfrom an electronic identifier associated with a user, the partialelectronic communication comprising a communication content andcomprising or associated with the electronic identifier associated withthe user and a second electronic identifier associated with a seconduser; extracting a communication context from the partial electroniccommunication; encoding the partial electronic communication forprocessing creating an encoded partial electronic communication;retrieving from a communication profile database a second communicationprofile for the second user using the second electronic identifierassociated with the second user, wherein the second communicationprofile comprises a second user communication attribute; processing theencoded partial electronic communication with a processor to generate acompositional change for the communication content of the partialelectronic communication using at least one of the communication contextor the second user communication attribute to generate the compositionalchange; and generating a changed electronic communication from thepartial electronic communication and the compositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to theelectronic identifier associated with the user, or transmitting thechanged electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles stored in a communication profile database which aresimilar to the communication profile. The processor may be trained onlarge-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language as determined by the processor from thesecond user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user asdetermined by the processor from the user communication attribute. Thepartial electronic communication further comprises a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a system of electronic communication assistance mayperform the following: receiving a partial electronic communication atan artificial intelligence assistant computing facility from anelectronic identifier associated with a user, the partial electroniccommunication comprising a communication content and comprising orassociated with the electronic identifier and a second electronicidentifier associated with a second user; retrieving a secondcommunication profile, wherein the second communication profilecomprises a second user communication attribute; processing the partialelectronic communication with a processor to generate a compositionalchange for the communication content of the partial electroniccommunication using the second user communication attribute to generatethe compositional change; and generating a changed electroniccommunication from the partial electronic communication and thecompositional change.

In embodiments, the system computer may be enabled to perform operationsincluding transmitting the changed electronic communication to theelectronic identifier associated with the user, or transmitting thechanged electronic communication to a second electronic identifierassociated with a second user. The compositional change may be derivedfrom representations of previous content and context from a plurality ofuser profiles which are similar to at least one of the communicationprofile or the second communication profile. The processor may betrained on large-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayuse at least one of a machine learning model, deep learning model, orother statistical learning algorithm for creating the compositionalchange. The compositional change may be an auto-generated textualcompletion. The auto-generated textual completion may be a phrasalcompletion. The processor may generate the compositional change byoptimizing generated language as determined by the processor from thesecond user communication attribute. The processor may generate thecompositional change by replicating a communication style of the user asdetermined by the processor from the user communication attribute. Thepartial electronic communication further comprises a communication goal,and the processor may generate the compositional change by optimizingfor impact and effectiveness of generated language with respect to thecommunication goal. The processor may generate the compositional changefurther using a communication template selected from a plurality ofcommunication templates comprising at least one of prepared text orplaceholder locations for defining structural elements for usercompletion. The processor may select the communication template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationtemplate based at least in part on the communication content. Theplurality of communication templates may include at least oneautomatically generated template generated by the processor. Theprocessor may select the communication template by using a machinelearning model to score the plurality of communication templates basedat least in part on the communication content, communication context,first user communication attribute, or second user communicationattribute.

In embodiments, a method of electronic communication assistance mayinclude intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted from a firstelectronic identifier associated with a first user to second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user; extracting acommunication context from the electronic communication; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a firstcommunication profile for the first user using the first useridentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving from the communication profiledatabase a second communication profile for the second user using thesecond electronic identifier, wherein the second communication profilecomprises a second user communication attribute; processing the encodedelectronic communication with a processor to generate a compositionalchange for the communication content of the electronic communicationusing at least one of the communication context, the first usercommunication attribute, or the second user communication attribute togenerate the compositional change; and generating a changed electroniccommunication from the electronic communication and the compositionalchange, wherein the changed electronic communication comprisesannotations to indicate the compositional change to the electroniccommunication.

In embodiments, the method may include transmitting the changedelectronic communication to the first electronic identifier associatedwith the first user or transmitting the changed electronic communicationto the second electronic identifier associated with the second user. Thecompositional change may be derived from representations of previouscontent and context from a plurality of user profiles stored in thecommunication profile database which are similar to at least one of thefirst communication profile or the second communication profile. Theprocessor may be trained on large-scale data mixed with priorcommunication and effective communications from the plurality of userprofiles. The processor may use at least one of a machine learninglanguage model or a statistical algorithm for creating the compositionalchange. The processor may generate the compositional change byoptimizing generated language of the second user as determined by theprocessor from the second user communication attribute. The processormay generate the compositional change by replicating a communicationstyle of the first user as determined by the processor from the firstuser communication attribute. The electronic communication furthercomprises a communication goal, and the processor may generate thecompositional change by optimizing for impact and effectiveness ofgenerated language with respect to the communication goal.

In embodiments, a method of electronic communication assistance mayinclude: intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted from a firstelectronic identifier associated with a first user to second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user; retrieving afirst communication profile for the first user using the first useridentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving a second communication profilefor the second user using the second electronic identifier, wherein thesecond communication profile comprises a second user communicationattribute; processing the electronic communication with a processor togenerate a compositional change for the communication content of theelectronic communication using at least one of the first usercommunication attribute or the second user communication attribute togenerate the compositional change; and generating a changed electroniccommunication from the electronic communication and the compositionalchange, wherein the changed electronic communication comprisesannotations to indicate the compositional change to the electroniccommunication.

In embodiments, a method of electronic communication assistance mayinclude: intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted from an electronicidentifier associated with a user, the electronic communicationcomprising a communication content and comprising or associated with theelectronic identifier associated with the user; extracting acommunication context from the electronic communication; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database acommunication profile for the user using the user identifier, whereinthe communication profile comprises a user communication attribute;processing the encoded electronic communication with a processor togenerate a compositional change for the communication content of theelectronic communication using at least one of the communication contextor the user communication attribute to generate the compositionalchange; and generating a changed electronic communication from theelectronic communication and the compositional change, wherein thechanged electronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted from an electronicidentifier associated with a user, the electronic communicationcomprising a communication content and comprising or associated with theelectronic identifier associated with the user; retrieving acommunication profile for the user using the user identifier, whereinthe communication profile comprises a user communication attribute;processing the electronic communication with a processor to generate acompositional change for the communication content of the electroniccommunication using the user communication attribute to generate thecompositional change; and generating a changed electronic communicationfrom the electronic communication and the compositional change, whereinthe changed electronic communication comprises annotations to indicatethe compositional change to the electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted from a firstelectronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; extracting a communication context from the electroniccommunication; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a communication profile for the seconduser using the second electronic identifier, wherein the communicationprofile comprises a second user communication attribute; processing theencoded electronic communication with a processor to generate acompositional change for the communication content of the electroniccommunication using at least one of the communication context or thesecond user communication attribute to generate the compositionalchange; and generating a changed electronic communication from theelectronic communication and the compositional change, wherein thechanged electronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: intercepting an electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the electronic communication was transmitted to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; retrieving from a communication profile database acommunication profile for the second user using the second electronicidentifier, wherein the communication profile comprises a second usercommunication attribute; processing the electronic communication with aprocessor to generate a compositional change for the communicationcontent of the electronic communication using the second usercommunication attribute to generate the compositional change; andgenerating a changed electronic communication from the electroniccommunication and the compositional change, wherein the changedelectronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted from a first electronic identifier associated with a firstuser to second electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; extracting a communication context from theelectronic communication; encoding the electronic communication forprocessing creating an encoded electronic communication; retrieving froma communication profile database a first communication profile for thefirst user using the first user identifier, wherein the firstcommunication profile comprises a first user communication attribute;retrieving from the communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute; processing the encoded electroniccommunication with a processor to generate a compositional change forthe communication content of the electronic communication using at leastone of the communication context, the first user communicationattribute, or the second user communication attribute to generate thecompositional change; and generating a changed electronic communicationfrom the electronic communication and the compositional change, whereinthe changed electronic communication comprises annotations to indicatethe compositional change to the electronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted from a first electronic identifier associated with a firstuser to second electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; retrieving a first communication profile for thefirst user using the first user identifier, wherein the firstcommunication profile comprises a first user communication attribute;retrieving a second communication profile for the second user using thesecond electronic identifier, wherein the second communication profilecomprises a second user communication attribute; processing theelectronic communication with a processor to generate a compositionalchange for the communication content of the electronic communicationusing at least one of the first user communication attribute or thesecond user communication attribute to generate the compositionalchange; and generating a changed electronic communication from theelectronic communication and the compositional change, wherein thechanged electronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted from an electronic identifier associated with a user, theelectronic communication comprising a communication content andcomprising or associated with the electronic identifier associated withthe user; extracting a communication context from the electroniccommunication; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a communication profile for the userusing the user identifier, wherein the communication profile comprises auser communication attribute; processing the encoded electroniccommunication with a processor to generate a compositional change forthe communication content of the electronic communication using at leastone of the communication context or the user communication attribute togenerate the compositional change; and generating a changed electroniccommunication from the electronic communication and the compositionalchange, wherein the changed electronic communication comprisesannotations to indicate the compositional change to the electroniccommunication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted from a electronic identifier associated with a user, theelectronic communication comprising a communication content andcomprising or associated with the electronic identifier associated withthe user; retrieving a communication profile for the user using the useridentifier, wherein the communication profile comprises a usercommunication attribute; processing the electronic communication with aprocessor to generate a compositional change for the communicationcontent of the electronic communication using the user communicationattribute to generate the compositional change; and generating a changedelectronic communication from the electronic communication and thecompositional change, wherein the changed electronic communicationcomprises annotations to indicate the compositional change to theelectronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted from a first electronic identifier associated with a firstuser to second electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; extracting a communication context from theelectronic communication; encoding the electronic communication forprocessing creating an encoded electronic communication; retrieving froma communication profile database a communication profile for the seconduser using the second electronic identifier, wherein the communicationprofile comprises a second user communication attribute; processing theencoded electronic communication with a processor to generate acompositional change for the communication content of the electroniccommunication using at least one of the communication context or thesecond user communication attribute to generate the compositionalchange; and generating a changed electronic communication from theelectronic communication and the compositional change, wherein thechanged electronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication wastransmitted to second electronic identifier associated with a seconduser, the electronic communication comprising a communication contentand comprising or associated with the first electronic identifierassociated with the first user; retrieving from a communication profiledatabase a communication profile for the second user using the secondelectronic identifier, wherein the communication profile comprises asecond user communication attribute; processing the electroniccommunication with a processor to generate a compositional change forthe communication content of the electronic communication using thesecond user communication attribute to generate the compositionalchange; and generating a changed electronic communication from theelectronic communication and the compositional change, wherein thechanged electronic communication comprises annotations to indicate thecompositional change to the electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a first electronic identifier associated with afirst user, the electronic communication information comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user and a secondelectronic identifier associated with a second user; extracting acommunication context from the electronic communication information;retrieving from a communication profile database a first communicationprofile for the first user using the first electronic identifier,wherein the first communication profile comprises a first usercommunication attribute; retrieving from the communication profiledatabase a second communication profile for the second user using thesecond electronic identifier, wherein the second communication profilecomprises a second user communication attribute; processing theelectronic communication information with a processor to generate acompositional recommendation for developing a communication contentrelated to the electronic communication information using at least oneof the communication context, the first user communication attribute, orthe second user communication attribute to generate the compositionalrecommendation; and transmitting the compositional recommendation to thefirst electronic identifier.

In embodiments, the method may further include intercepting anelectronic communication from the first electronic identifier, theelectronic communication relating to the electronic communicationinformation and the compositional recommendation; encoding theelectronic communication to create an encoded electronic communication;processing the encoded electronic communication with the processor toextract an additional communication attribute from the electroniccommunication; and updating the first communication profile for thefirst user based on the additional communication attribute. Thecompositional recommendation may be derived from representations ofprevious content and context from a plurality of user profiles stored inthe communication profile database which are similar to at least one ofthe first communication profile or the second communication profile. Theprocessor may calculate weighted scores for predicted reaction outcomeand expected reaction outcome, and derives the compositionalrecommendation based on a comparison of the calculated weighted scores.The processor may use at least one of a machine learning mode, deeplearning model, or statistical learning algorithm for creating thecompositional recommendation. The processor may generate thecompositional recommendation by evaluating a communication style of thefirst user as determined by the processor from the first usercommunication attribute. The processor may generate the compositionalrecommendation further using a communication recommendation templateselected from a plurality of communication recommendation templates. Theprocessor may select the communication recommendation template using atleast one of a machine learning model, deep learning model, orstatistical learning model to find a most effective communicationrecommendation template based at least in part on the electroniccommunication information. The plurality of communication recommendationtemplates may include at least one automatically generated templategenerated by the processor. The processor may select the communicationrecommendation template by using a machine learning model to score theplurality of communication recommendation templates based at least inpart on the electronic communication information. The electroniccommunication information may include a communication goal. Thecommunication goal may be used in generating the compositionalrecommendation.

In embodiments, a method of electronic communication assistance mayinclude: receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a first electronic identifier associated with afirst user, the electronic communication information comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user and a secondelectronic identifier associated with a second user; retrieving a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving a second communication profilefor the second user using the second electronic identifier, wherein thesecond communication profile comprises a second user communicationattribute; processing the electronic communication information with aprocessor to generate a compositional recommendation for developing acommunication content related to the electronic communicationinformation using at least one of the first user communication attributeor the second user communication attribute to generate the compositionalrecommendation; and transmitting the compositional recommendation to thefirst electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a electronic identifier associated with a user,the electronic communication information comprising a communicationcontent and comprising or associated with the electronic identifierassociated with the user; extracting a communication context from theelectronic communication information; retrieving from a communicationprofile database a communication profile for the user using theelectronic identifier, wherein the communication profile comprises auser communication attribute; processing the electronic communicationinformation with a processor to generate a compositional recommendationfor developing a communication content related to the electroniccommunication information using at least one of the communicationcontext or the user communication attribute to generate thecompositional recommendation; and transmitting the compositionalrecommendation to the electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a electronic identifier associated with a user,the electronic communication information comprising a communicationcontent and comprising or associated with the electronic identifierassociated with the user; retrieving a communication profile for theuser using the electronic identifier, wherein the communication profilecomprises a user communication attribute; processing the electroniccommunication information with a processor to generate a compositionalrecommendation for developing a communication content related to theelectronic communication information using the user communicationattribute; and transmitting the compositional recommendation to theelectronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a first electronic identifier associated with afirst user, the electronic communication information comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user and a secondelectronic identifier associated with a second user; extracting acommunication context from the electronic communication information;retrieving a communication profile for the second user using the secondelectronic identifier, wherein the communication profile comprises asecond user communication attribute; processing the electroniccommunication information with a processor to generate a compositionalrecommendation for developing a communication content related to theelectronic communication information using at least one of thecommunication context or the second user communication attribute togenerate the compositional recommendation; and transmitting thecompositional recommendation to the first electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude receiving electronic communication information as an electroniccommunication is composed at an artificial intelligence assistantcomputing facility from a first electronic identifier associated with afirst user, the electronic communication information comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user and a secondelectronic identifier associated with a second user; retrieving acommunication profile for the second user using the second electronicidentifier, wherein the communication profile comprises a second usercommunication attribute; processing the electronic communicationinformation with a processor to generate a compositional recommendationfor developing a communication content related to the electroniccommunication information using the second user communication attributeto generate the compositional recommendation; and transmitting thecompositional recommendation to the first electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the electronic communicationinformation comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and a second electronic identifier associated with a seconduser; extracting a communication context from the electroniccommunication information; retrieving from a communication profiledatabase a first communication profile for the first user using thefirst electronic identifier, wherein the first communication profilecomprises a first user communication attribute; retrieving from thecommunication profile database a second communication profile for thesecond user using the second electronic identifier, wherein the secondcommunication profile comprises a second user communication attribute;processing the electronic communication information with a processor togenerate a compositional recommendation for developing a communicationcontent related to the electronic communication information using atleast one of the communication context, the first user communicationattribute, or the second user communication attribute to generate thecompositional recommendation; and transmitting the compositionalrecommendation to the first electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the electronic communicationinformation comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and a second electronic identifier associated with a seconduser; retrieving a first communication profile for the first user usingthe first electronic identifier, wherein the first communication profilecomprises a first user communication attribute; retrieving a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute; processing the electronic communicationinformation with a processor to generate a compositional recommendationfor developing a communication content related to the electroniccommunication information using at least one of the first usercommunication attribute or the second user communication attribute togenerate the compositional recommendation; and transmitting thecompositional recommendation to the first electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from a electronic identifierassociated with a user, the electronic communication informationcomprising a communication content and comprising or associated with theelectronic identifier; extracting a communication context from theelectronic communication information; retrieving from a communicationprofile database a communication profile for the user using theelectronic identifier, wherein the communication profile comprises auser communication attribute; processing the electronic communicationinformation with a processor to generate a compositional recommendationfor developing a communication content related to the electroniccommunication information using at least one of the communicationcontext or the user communication attribute to generate thecompositional recommendation; and transmitting the compositionalrecommendation to the electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from an electronic identifierassociated with a user, the electronic communication informationcomprising a communication content and comprising or associated with theelectronic identifier associated with the user; retrieving acommunication profile for the user using the electronic identifier,wherein the communication profile comprises a user communicationattribute; processing the electronic communication information with aprocessor to generate a compositional recommendation for developing acommunication content related to the electronic communicationinformation using the user communication attribute to generate thecompositional recommendation; and transmitting the compositionalrecommendation to the electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the electronic communicationinformation comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and a second electronic identifier associated with a seconduser; extracting a communication context from the electroniccommunication information; retrieving from a communication profiledatabase a communication profile for the second user using the secondelectronic identifier, wherein the communication profile comprises asecond user communication attribute; processing the electroniccommunication information with a processor to generate a compositionalrecommendation for developing a communication content related to theelectronic communication information using at least one of thecommunication context or the second user communication attribute togenerate the compositional recommendation; and transmitting thecompositional recommendation to the first electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving electronic communicationinformation as an electronic communication is composed at an artificialintelligence assistant computing facility from a first electronicidentifier associated with a first user, the electronic communicationinformation comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and a second electronic identifier associated with a seconduser; retrieving a communication profile for the second user using thesecond electronic identifier, wherein the communication profilecomprises a second user communication attribute; processing theelectronic communication information with a processor to generate acompositional recommendation for developing a communication contentrelated to the electronic communication information using the seconduser communication attribute to generate the compositionalrecommendation; and transmitting the compositional recommendation to thefirst electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user and the secondelectronic identifier associated with the second user; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving from the communication profiledatabase a second communication profile for the second user using thesecond electronic identifier, wherein the second communication profilecomprises a second user communication attribute that identifies areceiving communication preference; processing the encoded electroniccommunication with a processor to generate a modified electroniccommunication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content, the first user communication attribute, or thesecond user communication attribute to process the encoded electroniccommunication; and transmitting the modified electronic communication tothe second electronic identifier.

In embodiments, the processor may generate the modified electroniccommunication by removing or replacing language from the electroniccommunication based at least in part on the second user communicationattribute. The removed or replaced language may be offensive or abusivelanguage. The processor may generate the modified electroniccommunication by summarizing language from the electronic communicationbased at least in part on the second user communication attribute. Theprocessor may generate the modified electronic communication byreformatting the electronic communication based at least in part on thesecond user communication attribute. The processor may generate themodified electronic communication by recomposing language from theelectronic communication based at least in part on the second usercommunication attribute. The processor may generate the modifiedelectronic communication by incorporating explanatory text associatedwith phases based at least in part on the second user communicationattribute. The processor may generate the modified electroniccommunication derived at least in part from representations of previouselectronic communications from a plurality of user profiles stored inthe communication profile database that are similar to at least one ofthe first communication profile or the second communication profile. Theprocessor may be trained on large-scale data mixed with priorcommunication and effective communications from the plurality of userprofiles. The processor may use at least one of a machine learningmodel, deep learning model, or statistical learning model for generatingthe modified electronic communication. The modified electroniccommunication may be used to generate at least one of an updated firstcommunication profile or an updated second communication profile. Priorto transmitting the modified electronic communication to the secondelectronic identifier, receiving a request by the second user may bereceived to provide the modified electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; retrieving a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving a second communication profilefor the second user using the second electronic identifier, wherein thesecond communication profile comprises a second user communicationattribute that identifies a receiving communication preference;processing the electronic communication with a processor to generate amodified electronic communication that is a modified version of theelectronic communication, wherein the processor uses at least one of thecommunication content, the first user communication attribute, or thesecond user communication attribute to process the electroniccommunication; and transmitting the modified electronic communication tothe second electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; processing the encoded electroniccommunication with a processor to generate a modified electroniccommunication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content or the first user communication attribute toprocess the encoded electronic communication; and transmitting themodified electronic communication to the second electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; retrieving a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; processing the electronic communicationwith a processor to generate a modified electronic communication that isa modified version of the electronic communication, wherein theprocessor uses at least one of the communication content or the firstuser communication attribute to process the electronic communication;and transmitting the modified electronic communication to the secondelectronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute that identifies a receiving communicationpreference; processing the encoded electronic communication with aprocessor to generate a modified electronic communication that is amodified version of the electronic communication, wherein the processoruses at least one of the communication content or the second usercommunication attribute to process the encoded electronic communication;and transmitting the modified electronic communication to the secondelectronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: intercepting a electronic communication at an artificialintelligence assistant computing facility, wherein the electroniccommunication was transmitted from a first electronic identifierassociated with a first user to a second electronic identifierassociated with a second user, the electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; retrieving asecond communication profile for the second user using the secondelectronic identifier, wherein the second communication profilecomprises a second user communication attribute that identifies areceiving communication preference; processing the electroniccommunication with a processor to generate a modified electroniccommunication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content or the second user communication attribute toprocess the electronic communication; and transmitting the modifiedelectronic communication to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user and the second electronic identifier associated with thesecond user; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a first communication profile for thefirst user using the first electronic identifier, wherein the firstcommunication profile comprises a first user communication attribute;retrieving from the communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute that identifies a receiving communicationpreference; processing the encoded electronic communication with aprocessor to generate a modified electronic communication that is amodified version of the electronic communication, wherein the processoruses at least one of the communication content, the first usercommunication attribute, or the second user communication attribute toprocess the encoded electronic communication; and transmitting themodified electronic communication to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; retrieving a first communication profile for the first userusing the first electronic identifier, wherein the first communicationprofile comprises a first user communication attribute; retrieving asecond communication profile for the second user using the secondelectronic identifier, wherein the second communication profilecomprises a second user communication attribute that identifies areceiving communication preference; processing the electroniccommunication with a processor to generate a modified electroniccommunication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content, the first user communication attribute, or thesecond user communication attribute to process the electroniccommunication; and transmitting the modified electronic communication tothe second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a first communication profile for thefirst user using the first electronic identifier, wherein the firstcommunication profile comprises a first user communication attribute;processing the encoded electronic communication with a processor togenerate a modified electronic communication that is a modified versionof the electronic communication, wherein the processor uses at least oneof the communication content or the first user communication attributeto process the encoded electronic communication; and transmitting themodified electronic communication to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; retrieving a first communication profile for the first userusing the first electronic identifier, wherein the first communicationprofile comprises a first user communication attribute; processing theelectronic communication with a processor to generate a modifiedelectronic communication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content or the first user communication attribute toprocess the electronic communication; and transmitting the modifiedelectronic communication to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a second communication profile for thesecond user using the second electronic identifier, wherein the secondcommunication profile comprises a second user communication attributethat identifies a receiving communication preference; processing theencoded electronic communication with a processor to generate a modifiedelectronic communication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content or the second user communication attribute toprocess the encoded electronic communication; and transmitting themodified electronic communication to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: intercepting a electroniccommunication at an artificial intelligence assistant computingfacility, wherein the electronic communication was transmitted from afirst electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; retrieving a second communication profile for the seconduser using the second electronic identifier, wherein the secondcommunication profile comprises a second user communication attributethat identifies a receiving communication preference; processing theelectronic communication with a processor to generate a modifiedelectronic communication that is a modified version of the electroniccommunication, wherein the processor uses at least one of thecommunication content or the second user communication attribute toprocess the electronic communication; and transmitting the modifiedelectronic communication to the second electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: receiving an audio-visual electronic communication at anartificial intelligence assistant computing platform from a first user,the first user identified by a first electronic identifier, theaudio-visual electronic communication comprising an audio communicationcontent and a video communication content and comprising or associatedwith the first electronic identifier associated with the first user anda second electronic identifier associated with a second user, whereinthe intended recipient of the audio-visual electronic communication isthe second user; encoding the audio communication content for processingcreating an encoded audio communication; extracting an audiocommunication information from the encoded audio communication content;encoding the video communication content for processing creating anencoded video communication; extracting a video communicationinformation from the encoded video communication content; processing theaudio-visual electronic communication with a processor to generate anaudio-visual compositional change for the communication content of theaudio-visual electronic communication using at least one of the audiocommunication content, the video communication content, audiocommunication information, video communication information, a sensorinput from a wearable user device, a first user communication attributeretrieved from a communication profile for the first user using thefirst electronic identifier or a second user communication attributeretrieved from a communication profile for the second user using thesecond electronic identifier to generate the compositional change;generating a changed electronic communication from the audio-visualelectronic communication and the compositional change; and providing thechanged electronic communication.

In embodiments, the compositional change may be derived fromrepresentations of previous content and context from a plurality of userprofiles stored in a communication profile database which are similar toat least one of the first communication profile or the secondcommunication profile. The processor may be trained on large-scale datamixed with prior communication and effective communications from aplurality of user profiles. The processor may use at least one of amachine learning model, deep learning model, or statistical learningalgorithm for creating the compositional change. The processor maygenerate the compositional change by optimizing generated language ofthe first user as determined by the processor from the first usercommunication attribute. The processor may generate the compositionalchange by replicating a communication style of the first user asdetermined by the processor from the first user communication attribute.The audio-visual electronic communication may include a communicationgoal, and the processor generates the compositional change by optimizingin respect of the communication goal. The method may further includeproviding a textual representation of the audio-visual electroniccommunication within a graphical user interface on a screen of acomputing device worn by the first user and providing the changedelectronic communication on the screen.

In embodiments, a method of electronic communication assistance mayinclude: receiving an audio-visual electronic communication at anartificial intelligence assistant computing platform from a first user,the audio-visual electronic communication comprising an audiocommunication content and a video communication content, wherein theintended recipient of the audio-visual electronic communication is asecond user; extracting an audio communication information from theaudio communication content; extracting a video communicationinformation from the video communication content; processing theaudio-visual electronic communication with a processor to generate anaudio-visual compositional change for the communication content of theaudio-visual electronic communication using at least one of the audiocommunication content, the video communication content, audiocommunication information, video communication information, a sensorinput from a wearable user device, a first user communication attributeretrieved from a communication profile for the first user or a seconduser communication attribute retrieved from a communication profile forthe second user to generate the compositional change; generating achanged electronic communication from the audio-visual electroniccommunication and the compositional change; and providing the changedelectronic communication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations comprising: receiving an audio-visual electroniccommunication at an artificial intelligence assistant computing platformfrom a first user, the first user identified by a first electronicidentifier, the audio-visual electronic communication comprising anaudio communication content and a video communication content andcomprising or associated with the first electronic identifier associatedwith the first user and a second electronic identifier associated with asecond user, wherein the intended recipient of the audio-visualelectronic communication is the second user; encoding the audiocommunication content for processing creating an encoded audiocommunication; extracting an audio communication information from theencoded audio communication content; encoding the video communicationcontent for processing creating an encoded video communication;extracting a video communication information from the encoded videocommunication content; processing the audio-visual electroniccommunication with a processor to generate an audio-visual compositionalchange for the communication content of the audio-visual electroniccommunication using at least one of the audio communication content, thevideo communication content, audio communication information, videocommunication information, a sensor input from a wearable user device, afirst user communication attribute retrieved from a communicationprofile for the first user using the first electronic identifier or asecond user communication attribute retrieved from a communicationprofile for the second user using the second electronic identifier togenerate the compositional change; generating a changed electroniccommunication from the audio-visual electronic communication and thecompositional change; and providing the changed electroniccommunication.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: receiving an audio-visual electroniccommunication at an artificial intelligence assistant computing platformfrom a first user, the audio-visual electronic communication comprisingan audio communication content and a video communication content,wherein the intended recipient of the audio-visual electroniccommunication is a second user; extracting an audio communicationinformation from the audio communication content; extracting a videocommunication information from the video communication content;processing the audio-visual electronic communication with a processor togenerate an audio-visual compositional change for the communicationcontent of the audio-visual electronic communication using at least oneof the audio communication content, the video communication content,audio communication information, video communication information, asensor input from a wearable user device, a first user communicationattribute retrieved from a communication profile for the first user or asecond user communication attribute retrieved from a communicationprofile for the second user to generate the compositional change;generating a changed electronic communication from the audio-visualelectronic communication and the compositional change; and providing thechanged electronic communication.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit audio-visual electroniccommunication at an artificial intelligence assistant computing platformfrom a first user, the first user identified by a first electronicidentifier, the audio-visual electronic communication comprising anaudio communication content and a video communication content andcomprising or associated with the first electronic identifier associatedwith the first user and a second electronic identifier associated with asecond user, wherein the intended recipient of the audio-visualelectronic communication is the second user; encoding the audiocommunication content for processing creating an encoded audiocommunication; extracting an audio communication context from theencoded audio communication content; encoding the video communicationcontent for processing creating an encoded video communication;extracting a video communication context from the encoded videocommunication content; processing the audio-visual electroniccommunication with a processor to generate a communication feedbackrelated to the audio-visual electronic communication using at least oneof the audio communication content, the video communication content,audio communication context, video communication context, a sensor inputfrom a wearable device, a first user communication attribute retrievedfrom a communication profile for the first user using the firstelectronic identifier or a second user communication attribute retrievedfrom a communication profile for the second user using the secondelectronic identifier to generate the communication feedback, whereinthe communication feedback provides communication information directedto the first user concerning the audio-visual communication; andproviding the audio communication content and the communicationfeedback. In embodiments, the communication feedback may be derived fromrepresentations of previous content and context from a plurality of userprofiles stored in a communication profile database which are similar toat least one of the first communication profile or the secondcommunication profile. The processor may be trained on large-scale datamixed with prior communication and effective communications from aplurality of user profiles. The processor may use at least one of amachine learning model, deep learning model, or statistical learningalgorithm for creating the communication feedback. The processor maygenerate the communication feedback by optimizing generated language ofthe first user as determined by the processor from the first usercommunication attribute. The processor may generate the communicationfeedback by optimizing a next electronic communication with respect tothe second user communication attribute. The audio-visual electroniccommunication may include a communication goal, and the processor maygenerate the communication feedback by optimizing in respect of thecommunication goal. The communication feedback may provide communicationinformation directed to the first user concerning the audio-visualcommunication related to generating a next audio-visual communication. Atextual representation of the audio-visual electronic communication maybe provided within a graphical user interface on a screen of a wearablecomputing device worn by the first user and providing the audiocommunication content and the communication feedback on the screen.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit audio-visual electroniccommunication at an artificial intelligence assistant computing platformfrom a first user, the audio-visual electronic communication comprisingan audio communication content and a video communication content,wherein the intended recipient of the audio-visual electroniccommunication is a second user; extracting an audio communicationcontext from the audio communication content; extracting a videocommunication context from the video communication content; processingthe audio-visual electronic communication with a processor to generate acommunication feedback related to the audio-visual electroniccommunication using at least one of the audio communication content, thevideo communication content, audio communication context, videocommunication context, a sensor input from a wearable device, a firstuser communication attribute retrieved from a communication profile forthe first user or a second user communication attribute retrieved from acommunication profile for the second user to generate the communicationfeedback, wherein the communication feedback provides communicationinformation directed to the first user concerning the audio-visualcommunication; and providing the audio communication content and thecommunication feedback.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitaudio-visual electronic communication at an artificial intelligenceassistant computing platform from a first user, the first useridentified by a first electronic identifier, the audio-visual electroniccommunication comprising an audio communication content and a videocommunication content and comprising or associated with the firstelectronic identifier associated with the first user and a secondelectronic identifier associated with a second user, wherein theintended recipient of the audio-visual electronic communication is thesecond user; encoding the audio communication content for processingcreating an encoded audio communication; extracting an audiocommunication context from the encoded audio communication content;encoding the video communication content for processing creating anencoded video communication; extracting a video communication contextfrom the encoded video communication content; processing theaudio-visual electronic communication with a processor to generate acommunication feedback related to the audio-visual electroniccommunication using at least one of the audio communication content, thevideo communication content, audio communication context, videocommunication context, a sensor input from a wearable device, a firstuser communication attribute retrieved from a communication profile forthe first user using the first electronic identifier or a second usercommunication attribute retrieved from a communication profile for thesecond user using the second electronic identifier to generate thecommunication feedback, wherein the communication feedback providescommunication information directed to the first user concerning theaudio-visual communication; and providing the audio communicationcontent and the communication feedback.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitaudio-visual electronic communication at an artificial intelligenceassistant computing platform from a first user, the audio-visualelectronic communication comprising an audio communication content and avideo communication content, wherein the intended recipient of theaudio-visual electronic communication is a second user; extracting anaudio communication context from the audio communication content;extracting a video communication context from the video communicationcontent; processing the audio-visual electronic communication with aprocessor to generate a communication feedback related to theaudio-visual electronic communication using at least one of the audiocommunication content, the video communication content, audiocommunication context, video communication context, a sensor input froma wearable device, a first user communication attribute retrieved from acommunication profile for the first user or a second user communicationattribute retrieved from a communication profile for the second user togenerate the communication feedback, wherein the communication feedbackprovides communication information directed to the first user concerningthe audio-visual communication; and providing the audio communicationcontent and the communication feedback.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a first electronicidentifier associated with a first user to a second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user and thesecond electronic identifier associated with the second user; extractinga communication context from the electronic communication; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a firstcommunication profile for the first user utilizing the first electronicidentifier, wherein the first communication profile comprises a firstuser communication attribute; retrieving from the communication profiledatabase a second communication profile for the second user utilizingthe second electronic identifier, wherein the second communicationprofile comprises a second user communication attribute; receivingelectronic reaction data from a second electronic identifier of thesecond user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;encoding the electronic reaction data for processing creating an encodedelectronic reaction data; processing the encoded electroniccommunication and the encoded electronic reaction data to extract areaction context with a processor to generate a response feedbackcommunication using at least one of the communication content, theextracted communication context, the first user communication attribute,or the second user communication attribute; and sending the responsefeedback communication to the first electronic identifier.

In embodiments, the response feedback communication may be derived atleast in part from representations of previous electronic communicationsfrom a plurality of user profiles stored in the communication profiledatabase which are similar to at least one of the first communicationprofile or the second communication profile. The processor may betrained on large-scale data mixed with prior communication and effectivecommunications from the plurality of user profiles. The processor mayutilize at least one of a machine learning language model or astatistical algorithm for creating the response feedback communication.The electronic communication may include a communication goal, and theprocessor generates the response feedback communication at least in partby computing and utilizing a reaction difference attribute derived fromdetermined differences between the communication goal and the electronicreaction data. The electronic reaction data may include at least one oflocation information for the second user, response time between thesecond user receiving the electronic communication and reading theelectronic communication, or an action taken by the second user. Thereaction context may include an emotional state of the second user. Theelectronic reaction data may be used to generate at least one of anupdated first communication profile or an updated second communicationprofile. At least one of the updated first communication profile or theupdated second communication profile may be used to predict a mostlikely reaction outcome in a second electronic communication. Theelectronic reaction data may be used to train a machine learning modelthat is configured to at least one of generate a communication contentor modify a communication content.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a first electronicidentifier associated with a first user to a second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user; retrieving afirst communication profile for the first user utilizing the firstelectronic identifier, wherein the first communication profile comprisesa first user communication attribute; retrieving a second communicationprofile for the second user utilizing the second electronic identifier,wherein the second communication profile comprises a second usercommunication attribute; receiving electronic reaction data from asecond electronic identifier of the second user, wherein the electronicreaction data is generated in response to the second user receiving theelectronic communication; processing the electronic communication andthe electronic reaction data to extract a reaction context with aprocessor to generate a response feedback communication using at leastone of the communication content, the first user communicationattribute, or the second user communication attribute; and sending theresponse feedback communication to the first electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a electronic identifierassociated with a user, the electronic communication comprising acommunication content and comprising or associated with the electronicidentifier associated with the user; extracting a communication contextfrom the electronic communication; encoding the electronic communicationfor processing creating an encoded electronic communication; retrievingfrom a communication profile database a communication profile for theuser utilizing the electronic identifier, wherein the communicationprofile comprises a user communication attribute; receiving electronicreaction data from a second electronic identifier of the second user,wherein the electronic reaction data is generated in response to thesecond user receiving the electronic communication; encoding theelectronic reaction data for processing creating an encoded electronicreaction data; processing the encoded electronic communication and theencoded electronic reaction data to extract a reaction context with aprocessor to generate a response feedback communication using at leastone of the communication content, the extracted communication context,or the user communication attribute; and sending the response feedbackcommunication to the electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a electronic identifierassociated with a user, the electronic communication comprising acommunication content and comprising or associated with the electronicidentifier associated with the user; retrieving a communication profilefor the user utilizing the electronic identifier, wherein thecommunication profile comprises a user communication attribute;receiving electronic reaction data from a second electronic identifierof the second user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;processing the electronic communication and the electronic reaction datato extract a reaction context with a processor to generate a responsefeedback communication using at least one of the communication contentor the user communication attribute; and sending the response feedbackcommunication to the electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a first electronicidentifier associated with a first user to a second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user; extracting acommunication context from the electronic communication; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database acommunication profile for the second user utilizing the secondelectronic identifier, wherein the communication profile comprises asecond user communication attribute; receiving electronic reaction datafrom a second electronic identifier of the second user, wherein theelectronic reaction data is generated in response to the second userreceiving the electronic communication; encoding the electronic reactiondata for processing creating an encoded electronic reaction data;processing the encoded electronic communication and the encodedelectronic reaction data to extract a reaction context with a processorto generate a response feedback communication using at least one of thecommunication content, the extracted communication context, or thesecond user communication attribute; and sending the response feedbackcommunication to the first electronic identifier.

In embodiments, a method of electronic communication assistance mayinclude: obtaining a copy of an in-transit electronic communication atan artificial intelligence assistant computing facility, wherein theelectronic communication was transmitted from a first electronicidentifier associated with a first user to a second electronicidentifier associated with a second user, the electronic communicationcomprising a communication content and comprising or associated with thefirst electronic identifier associated with the first user; retrieving acommunication profile for the second user utilizing the secondelectronic identifier, wherein the communication profile comprises asecond user communication attribute; receiving electronic reaction datafrom a second electronic identifier of the second user, wherein theelectronic reaction data is generated in response to the second userreceiving the electronic communication; processing the electroniccommunication and the electronic reaction data to extract a reactioncontext with a processor to generate a response feedback communicationusing at least one of the communication content or the second usercommunication attribute; and sending the response feedback communicationto the first electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a first electronic identifier associated with a first user to asecond electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user and the second electronic identifier associated withthe second user; extracting a communication context from the electroniccommunication; encoding the electronic communication for processingcreating an encoded electronic communication; retrieving from acommunication profile database a first communication profile for thefirst user utilizing the first electronic identifier, wherein the firstcommunication profile comprises a first user communication attribute;retrieving from the communication profile database a secondcommunication profile for the second user utilizing the secondelectronic identifier, wherein the second communication profilecomprises a second user communication attribute; receiving electronicreaction data from a second electronic identifier of the second user,wherein the electronic reaction data is generated in response to thesecond user receiving the electronic communication; encoding theelectronic reaction data for processing creating an encoded electronicreaction data; processing the encoded electronic communication and theencoded electronic reaction data to extract a reaction context with aprocessor to generate a response feedback communication using at leastone of the communication content, the extracted communication context,the first user communication attribute, or the second user communicationattribute; and sending the response feedback communication to the firstelectronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a first electronic identifier associated with a first user to asecond electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; retrieving a first communication profile for thefirst user utilizing the first electronic identifier, wherein the firstcommunication profile comprises a first user communication attribute;retrieving a second communication profile for the second user utilizingthe second electronic identifier, wherein the second communicationprofile comprises a second user communication attribute; receivingelectronic reaction data from a second electronic identifier of thesecond user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;processing the electronic communication and the electronic reaction datato extract a reaction context with a processor to generate a responsefeedback communication using at least one of the communication content,the first user communication attribute, or the second user communicationattribute; and sending the response feedback communication to the firstelectronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a electronic identifier associated with a user, the electroniccommunication comprising a communication content and comprising orassociated with the electronic identifier associated with the user;extracting a communication context from the electronic communication;encoding the electronic communication for processing creating an encodedelectronic communication; retrieving from a communication profiledatabase a communication profile for the user utilizing the electronicidentifier, wherein the communication profile comprises a usercommunication attribute; receiving electronic reaction data from asecond electronic identifier of the second user, wherein the electronicreaction data is generated in response to the second user receiving theelectronic communication; encoding the electronic reaction data forprocessing creating an encoded electronic reaction data; processing theencoded electronic communication and the encoded electronic reactiondata to extract a reaction context with a processor to generate aresponse feedback communication using at least one of the communicationcontent, the extracted communication context or the user communicationattribute; and sending the response feedback communication to theelectronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a electronic identifier associated with a user, the electroniccommunication comprising a communication content and comprising orassociated with the electronic identifier associated with the user;retrieving from a communication profile database a communication profilefor the user utilizing the electronic identifier, wherein thecommunication profile comprises a user communication attribute;receiving electronic reaction data from a second electronic identifierof the second user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;processing the electronic communication and the electronic reaction datato extract a reaction context with a processor to generate a responsefeedback communication using at least one of the communication content,the extracted communication context or the user communication attribute;and sending the response feedback communication to the electronicidentifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a first electronic identifier associated with a first user to asecond electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; extracting a communication context from theelectronic communication; encoding the electronic communication forprocessing creating an encoded electronic communication; retrieving froma communication profile database a communication profile for the seconduser utilizing the second electronic identifier, wherein thecommunication profile comprises a second user communication attribute;receiving electronic reaction data from a second electronic identifierof the second user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;encoding the electronic reaction data for processing creating an encodedelectronic reaction data; processing the encoded electroniccommunication and the encoded electronic reaction data to extract areaction context with a processor to generate a response feedbackcommunication using at least one of the communication content, theextracted communication context or the second user communicationattribute; and sending the response feedback communication to the firstelectronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: obtaining a copy of an in-transitelectronic communication at an artificial intelligence assistantcomputing facility, wherein the electronic communication was transmittedfrom a first electronic identifier associated with a first user to asecond electronic identifier associated with a second user, theelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; retrieving a communication profile for the seconduser utilizing the second electronic identifier, wherein thecommunication profile comprises a second user communication attribute;receiving electronic reaction data from a second electronic identifierof the second user, wherein the electronic reaction data is generated inresponse to the second user receiving the electronic communication;processing the electronic communication and the electronic reaction datato extract a reaction context with a processor to generate a responsefeedback communication using at least one of the communication contentor the second user communication attribute; and sending the responsefeedback communication to the first electronic identifier.

In embodiments, a computer-implemented method for modifying an incomingcommunication through a graphical user interface may include:intercepting a first electronic communication from further transmissionat an artificial intelligence assistant computing facility, wherein thefirst electronic communication was transmitted from a first electronicidentifier associated with a first user to a second electronicidentifier associated with a second user, the first electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; encoding the first electronic communication for processingcreating an encoded first electronic communication; retrieving from acommunication profile database a second communication profile for thesecond user using the second electronic identifier, wherein the secondcommunication profile comprises a second user communication attributethat identifies a receiving communication preference; processing theencoded first electronic communication with a processor; displaying thefirst electronic communication within a graphical user interface on ascreen of a computing device of the second user, wherein the displayingthe first electronic communication comprises displaying thecommunication content and displaying a communication transformationquery directed to the second user to determine if the first electroniccommunication should be transformed; receiving a communicationtransformation indication from the computing device that directs theprocessor to transform the first electronic communication to a modifiedelectronic communication that is a modified version of the firstelectronic communication, wherein the processor uses the communicationcontent and the second user communication attribute to transform thefirst electronic communication; and displaying the modified electroniccommunication within the graphical user interface on the screen of thecomputing device.

In embodiments, the processor may transform the first electroniccommunication by removing language from the first electroniccommunication based at least in part on the second user communicationattribute. The removed language may be offensive language. The processormay transform the first electronic communication by summarizing languagefrom the first electronic communication based at least in part on thesecond user communication attribute. The processor may transform thefirst electronic communication by reformatting the first electroniccommunication based at least in part on the second user communicationattribute. The processor may transform the first electroniccommunication by recomposing language from the first electroniccommunication based at least in part on the second user communicationattribute. The processor may transform the first electroniccommunication by incorporating explanatory text associated with phasesbased at least in part on the second user communication attribute. Theprocessor may transform the first electronic communication derived atleast in part from representations of previous electronic communicationsfrom a plurality of user profiles stored in the communication profiledatabase which are similar to the second communication profile. Theprocessor may be trained on large-scale data mixed with priorcommunication and effective communications from the plurality of userprofiles. The processor may use at least one of a machine learninglanguage model or a statistical algorithm to transform the firstelectronic communication. The transformed first electronic communicationmay be used to generate an updated second communication profile. Theupdated second communication profile may be used to predict a mostlikely modification outcome in a second electronic communication.Transformation data may be extracted from the transformation to thefirst electronic communication, wherein the transformation data is usedto train a language model used by the processor.

In embodiments, a computer-implemented method for modifying atransmitted communication through a graphical user interface mayinclude: intercepting a first electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the first electronic communication was transmitted from a firstelectronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the firstelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; encoding the first electronic communication forprocessing creating an encoded first electronic communication;retrieving from a communication profile database a second communicationprofile for the second user using the second electronic identifier,wherein the second communication profile comprises a second usercommunication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; displaying the first electronic communication within agraphical user interface on a screen of a computing device of the firstuser, wherein the displaying the first electronic communicationcomprises displaying the communication content and displaying acommunication transformation query directed to the first user todetermine if the first electronic communication should be transformedand resent; receiving a communication transformation indication from thecomputing device that directs the processor to transform the firstelectronic communication to a modified electronic communication that isa modified version of the first electronic communication, wherein theprocessor uses the communication content and the second usercommunication attribute to transform the first electronic communication;and transmitting the modified electronic communication to the secondelectronic identifier.

In embodiments, the processor may transform the first electroniccommunication derived at least in part from representations of previouselectronic communications from a plurality of user profiles stored inthe communication profile database which are similar to the secondcommunication profile. The processor may be trained on large-scale datamixed with prior communication and effective communications from theplurality of user profiles. The processor may use at least one of amachine learning language model or a statistical algorithm to transformthe first electronic communication. The transformed first electroniccommunication may be used to generate an updated second communicationprofile. The updated second communication profile may be used to predicta most likely modification outcome in a second electronic communication.Transformation data may be extracted from the transformation to thefirst electronic communication, wherein the transformation data is usedto train a language model used by the processor.

In embodiments, a computer-implemented method for modifying atransmitted communication through a graphical user interface mayinclude: intercepting a first electronic communication from furthertransmission at an artificial intelligence assistant computing facilitywherein, the first electronic communication was transmitted from a firstelectronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the electroniccommunication comprising a communication content and comprising orassociated with the first electronic identifier associated with thefirst user; retrieving a communication profile for a user using thesecond electronic identifier, wherein the communication profilecomprises a second user communication attribute that identifies areceiving communication preference; processing the first electroniccommunication with a processor; displaying the first electroniccommunication within a graphical user interface on a screen of a secondcomputing device of the second user; displaying a communicationtransformation query directed to the second user; receiving acommunication transformation indication from a first computing device ofthe first user that directs the processor to transform the electroniccommunication to a modified electronic communication; and displaying themodified electronic communication within the graphical user interface onthe screen of the second computing device.

In embodiments, a computer-implemented method for modifying atransmitted communication through a graphical user interface mayinclude: intercepting a first electronic communication from furthertransmission at an artificial intelligence assistant computing facility,wherein the first electronic communication was transmitted from a firstelectronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the firstelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; retrieving a communication profile for the seconduser using the second electronic identifier, wherein the communicationprofile comprises a second user communication attribute that identifiesa receiving communication preference; processing the first electroniccommunication with a processor; displaying the first electroniccommunication within a graphical user interface on a screen of acomputing device of the first user; displaying a communicationtransformation query directed to the first user to determine if thefirst electronic communication should be transformed and resent;receiving a communication transformation indication from the computingdevice that directs the processor to transform the first electroniccommunication to a modified electronic communication; and transmittingthe modified electronic communication to the second electronicidentifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: intercepting a first electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the first electronic communicationwas transmitted from a first electronic identifier associated with afirst user to a second electronic identifier associated with a seconduser, the first electronic communication comprising a communicationcontent and comprising or associated with the first electronicidentifier associated with the first user; encoding the first electroniccommunication for processing creating an encoded first electroniccommunication; retrieving from a communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; displaying the first electronic communication within agraphical user interface on a screen of a computing device of the seconduser, wherein the displaying the first electronic communicationcomprises displaying the communication content and displaying acommunication transformation query directed to the second user todetermine if the first electronic communication should be transformed;receiving a communication transformation indication from the computingdevice that directs the processor to transform the first electroniccommunication to a modified electronic communication that is a modifiedversion of the first electronic communication, wherein the processoruses the communication content and the second user communicationattribute to transform the first electronic communication; anddisplaying the modified electronic communication within the graphicaluser interface on the screen of the computing device.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: intercepting a first electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the first electronic communicationwas transmitted from a first electronic identifier associated with afirst user to a second electronic identifier associated with a seconduser, the first electronic communication comprising a communicationcontent and comprising or associated with the first electronicidentifier associated with the first user; encoding the first electroniccommunication for processing creating an encoded first electroniccommunication; retrieving from a communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; displaying the first electronic communication within agraphical user interface on a screen of a computing device of the firstuser, wherein the displaying the first electronic communicationcomprises displaying the communication content and displaying acommunication transformation query directed to the first user todetermine if the first electronic communication should be transformedand resent; receiving a communication transformation indication from thecomputing device that directs the processor to transform the firstelectronic communication to a modified electronic communication that isa modified version of the first electronic communication, wherein theprocessor uses the communication content and the second usercommunication attribute to transform the first electronic communication;and transmitting the modified electronic communication to the secondelectronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: intercepting a first electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the first electronic communicationwas transmitted from a first electronic identifier associated with afirst user to a second electronic identifier associated with a seconduser, the first electronic communication comprising a communicationcontent and comprising or associated with the first electronicidentifier associated with the first user; retrieving from acommunication profile database a communication profile for the seconduser using the second electronic identifier, wherein the communicationprofile comprises a second user communication attribute that identifiesa receiving communication preference; processing the first electroniccommunication with a processor; displaying the first electroniccommunication within a graphical user interface on a screen of a secondcomputing device of the second user; displaying a communicationtransformation query directed to the second user; receiving acommunication transformation indication from a first computing device ofthe first user that directs the processor to transform the firstelectronic communication to a modified electronic communication; anddisplaying the modified electronic communication within the graphicaluser interface on the screen of the second computing device.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: intercepting a first electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the first electronic communicationwas transmitted from a first electronic identifier associated with afirst user to a second electronic identifier associated with a seconduser, the first electronic communication comprising a communicationcontent and comprising or associated with the first electronicidentifier associated with the first user; retrieving a communicationprofile for the second user using the second electronic identifier,wherein the communication profile comprises a second user communicationattribute that identifies a receiving communication preference;processing the first electronic communication with a processor;displaying the first electronic communication within a graphical userinterface on a screen of a computing device of the first user;displaying a communication transformation query directed to the firstuser to determine if the first electronic communication should betransformed and resent; receiving a communication transformationindication from the computing device that directs the processor totransform the first electronic communication to a modified electroniccommunication; and transmitting the modified electronic communication tothe second electronic identifier.

In embodiments, a computer-implemented method for modifying an incomingcommunication through a graphical user interface may include: receivinga first electronic communication at an artificial intelligence assistantcomputing facility, wherein the first electronic communication isassociated with a first electronic identifier associated with a firstuser and directed to a second electronic identifier associated with asecond user, the first electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; encoding the firstelectronic communication for processing creating an encoded firstelectronic communication; retrieving from a communication profiledatabase a second communication profile for the second user using thesecond electronic identifier, wherein the second communication profilecomprises a second user communication attribute that identifies areceiving communication preference; processing the encoded firstelectronic communication with a processor; presenting the firstelectronic communication through an electronic communication link withina graphical user interface on a screen of a computing device of thesecond user, wherein the first electronic communication comprisesdisplaying the communication content and displaying a communicationtransformation query directed to the second user to determine if thefirst electronic communication should be transformed; receiving acommunication transformation indication from the computing device thatdirects the processor to transform the first electronic communication toa modified electronic communication that is a modified version of thefirst electronic communication, wherein the processor uses thecommunication content and the second user communication attribute totransform the first electronic communication; and presenting themodified electronic communication through the electronic communicationlink within the graphical user interface on the screen of the computingdevice of the second user.

In embodiments, a computer-implemented method for modifying an incomingcommunication through a graphical user interface may include: receivinga first electronic communication at an artificial intelligence assistantcomputing facility, wherein the first electronic communication isassociated with a first electronic identifier associated with a firstuser to a second electronic identifier associated with a second user,the first electronic communication comprising a communication contentand comprising or associated with the first electronic identifierassociated with the first user; encoding the first electroniccommunication for processing creating an encoded first electroniccommunication; retrieving from a communication profile database a secondcommunication profile for the second user using the second electronicidentifier, wherein the second communication profile comprises a seconduser communication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; presenting the first electronic communication through anelectronic communication link within a graphical user interface on ascreen of a computing device of the first user, wherein the firstelectronic communication comprises displaying the communication contentand displaying a communication transformation query directed to thefirst user to determine if the first electronic communication should betransformed and resent; receiving a communication transformationindication from the computing device of the first user that directs theprocessor to transform the first electronic communication to a modifiedelectronic communication that is a modified version of the firstelectronic communication, wherein the processor uses at least one ofinput from the first user, the communication content, or the second usercommunication attribute to transform the first electronic communication;and presenting the modified electronic communication through theelectronic communication link to the second electronic identifier.

In embodiments, a computer-implemented method for modifying an incomingcommunication through a graphical user interface may include: receivinga first electronic communication at an artificial intelligence assistantcomputing facility, wherein the first electronic communication isassociated with a first electronic identifier associated with a firstuser and directed to a second electronic identifier associated with asecond user, the first electronic communication comprising acommunication content and comprising or associated with the firstelectronic identifier associated with the first user; encoding the firstelectronic communication for processing creating an encoded firstelectronic communication; retrieving from a communication profiledatabase a second communication profile for the second user using thesecond electronic identifier; processing the encoded first electroniccommunication with a processor; presenting the first electroniccommunication through an electronic communication link within agraphical user interface on a screen of a computing device of the seconduser; receiving a communication transformation indication from thecomputing device that directs the processor to transform the firstelectronic communication to a modified electronic communication that isa modified version of the first electronic communication; and presentingthe modified electronic communication through the electroniccommunication link within the graphical user interface on the screen ofthe computing device of the second user.

In embodiments, a computer-implemented method for modifying an incomingcommunication through a graphical user interface may include: receivinga first electronic communication at an artificial intelligence assistantcomputing facility, wherein the first electronic communication isassociated with a first electronic identifier associated with a firstuser to a second electronic identifier associated with a second user,the first electronic communication comprising a communication contentand comprising or associated with the first electronic identifierassociated with the first user; retrieving a second communicationprofile for the second user using the second electronic identifier,wherein the second communication profile comprises a second usercommunication attribute that identifies a receiving communicationpreference; processing the first electronic communication with aprocessor; presenting the first electronic communication through anelectronic communication link within a graphical user interface on ascreen of a computing device of the first user; receiving acommunication transformation indication from the computing device of thefirst user that directs the processor to transform the first electroniccommunication to a modified electronic communication that is a modifiedversion of the first electronic communication; and presenting themodified electronic communication through the electronic communicationlink to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: receiving a first electroniccommunication at an artificial intelligence assistant computingfacility, wherein the first electronic communication is associated witha first electronic identifier associated with a first user and directedto a second electronic identifier associated with a second user, thefirst electronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; encoding the first electronic communication forprocessing creating an encoded first electronic communication;retrieving from a communication profile database a second communicationprofile for the second user using the second electronic identifier,wherein the second communication profile comprises a second usercommunication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; presenting the first electronic communication through anelectronic communication link within a graphical user interface on ascreen of a computing device of the second user, wherein the firstelectronic communication comprises displaying the communication contentand displaying a communication transformation query directed to thesecond user to determine if the first electronic communication should betransformed; receiving a communication transformation indication fromthe computing device that directs the processor to transform the firstelectronic communication to a modified electronic communication that isa modified version of the first electronic communication, wherein theprocessor uses the communication content and the second usercommunication attribute to transform the first electronic communication;and presenting the modified electronic communication through theelectronic communication link within the graphical user interface on thescreen of the computing device of the second user.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: receiving a first electroniccommunication at an artificial intelligence assistant computingfacility, wherein the first electronic communication is associated witha first electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the firstelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; encoding the first electronic communication forprocessing creating an encoded first electronic communication;retrieving from a communication profile database a second communicationprofile for the second user using the second electronic identifier,wherein the second communication profile comprises a second usercommunication attribute that identifies a receiving communicationpreference; processing the encoded first electronic communication with aprocessor; presenting the first electronic communication through anelectronic communication link within a graphical user interface on ascreen of a computing device of the first user, wherein the firstelectronic communication comprises displaying the communication contentand displaying a communication transformation query directed to thefirst user to determine if the first electronic communication should betransformed and resent; receiving a communication transformationindication from the computing device of the first user that directs theprocessor to transform the first electronic communication to a modifiedelectronic communication that is a modified version of the firstelectronic communication, wherein the processor uses at least one ofinput from the first user, the communication content, or the second usercommunication attribute to transform the first electronic communication;and presenting the modified electronic communication through theelectronic communication link to the second electronic identifier.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: receiving a first electroniccommunication at an artificial intelligence assistant computingfacility, wherein the first electronic communication is associated witha first electronic identifier associated with a first user and directedto a second electronic identifier associated with a second user, thefirst electronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; encoding the first electronic communication forprocessing creating an encoded first electronic communication;retrieving from a communication profile database a second communicationprofile for the second user using the second electronic identifier;processing the encoded first electronic communication with a processor;presenting the first electronic communication through an electroniccommunication link within a graphical user interface on a screen of acomputing device of the second user; receiving a communicationtransformation indication from the computing device that directs theprocessor to transform the first electronic communication to a modifiedelectronic communication that is a modified version of the firstelectronic communication; and presenting the modified electroniccommunication through the electronic communication link within thegraphical user interface on the screen of the computing device of thesecond user.

In embodiments, a system may include a server computer comprising aprocessor and a computer-readable storage device that storesinstructions that, when executed by the processor, cause the processorto perform operations including: receiving a first electroniccommunication at an artificial intelligence assistant computingfacility, wherein the first electronic communication is associated witha first electronic identifier associated with a first user to a secondelectronic identifier associated with a second user, the firstelectronic communication comprising a communication content andcomprising or associated with the first electronic identifier associatedwith the first user; retrieving a second communication profile for thesecond user using the second electronic identifier, wherein the secondcommunication profile comprises a second user communication attributethat identifies a receiving communication preference; processing thefirst electronic communication with a processor; presenting the firstelectronic communication through an electronic communication link withina graphical user interface on a screen of a computing device of thefirst user; receiving a communication transformation indication from thecomputing device of the first user that directs the processor totransform the first electronic communication to a modified electroniccommunication that is a modified version of the first electroniccommunication; and presenting the modified electronic communicationthrough the electronic communication link to the second electronicidentifier.

In any system or method embodiments described herein, the system ormethod may further include any of the described features, such as, butnot limited to, transmitting the changed electronic communication to thefirst electronic identifier associated with the first user, and/ortransmitting the changed electronic communication to the secondelectronic identifier associated with the second user. The compositionalchange may be derived from representations of previous content andcontext from a plurality of user profiles stored in the communicationprofile database which are similar to at least one of the firstcommunication profile or the second communication profile. The processormay be trained on large-scale data mixed with prior communication andeffective communications from the plurality of user profiles. Theprocessor may use at least one of a machine learning model, deeplearning model, or other statistical learning algorithm for creating thecompositional change. The compositional change may be an auto-generatedtextual completion; the auto-generated textual completion may be aphrasal completion, and the processor may generate the compositionalchange by optimizing generated language as determined by the processorfrom the second user communication attribute. The processor may generatethe compositional change by replicating a communication style of thefirst user as determined by the processor from the first usercommunication attribute. The partial electronic communication mayinclude a communication goal, and the processor may generate thecompositional change by optimizing for impact and effectiveness ofgenerated language with respect to the communication goal. The processormay generate the compositional change further using a communicationtemplate selected from a plurality of communication templates comprisingat least one of prepared text or placeholder locations for definingstructural elements for user completion. The processor may select thecommunication template using at least one of a machine learning model,deep learning model, or statistical learning model to find a mosteffective communication template based at least in part on thecommunication content. The plurality of communication templates mayinclude at least one automatically generated template generated by theprocessor. The processor may select the communication template by usinga machine learning model, deep learning model, or statistical learningalgorithm to score the plurality of communication templates based atleast in part on the communication content, communication context, firstuser communication attribute, or second user communication attribute,communication context, first user communication attribute, or seconduser communication attribute.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate generally, by way of example, but not by way oflimitation, a detailed description of certain embodiments discussed inthe present document.

FIG. 1 depicts a prior art example of a writing assistant.

FIG. 2 illustrates a functional flow diagram of an artificialintelligent assistant according to exemplary and non-limitingembodiments.

FIG. 3 illustrates aspects of an artificial intelligent assistantaccording to exemplary and non-limiting embodiments.

FIG. 4 illustrates an overview of a comprehensive communicationassistance workflow according to exemplary and non-limiting embodiments.

FIG. 5 illustrates an overview of a composition workflow according toexemplary and non-limiting embodiments.

FIG. 6 illustrates an overview of a editing workflow according toexemplary and non-limiting embodiments.

FIG. 7 illustrates a reaction prediction workflow according to exemplaryand non-limiting embodiments.

FIG. 8 illustrates a reaction prediction workflow with ranking accordingto exemplary and non-limiting embodiments.

FIG. 9 illustrates a reaction capture workflow according to exemplaryand non-limiting embodiments.

FIG. 10 illustrates a workflow for optimizing an incoming communicationaccording to exemplary and non-limiting embodiments.

FIG. 11 illustrates a workflow for optimizing an incoming communicationand feedback generation according to exemplary and non-limitingembodiments.

FIG. 12 illustrates a receiver interface workflow according to exemplaryand non-limiting embodiments.

FIG. 13 illustrates an audio-visual communication workflow according toexemplary and non-limiting embodiments.

FIGS. 14-20 illustrate user interface views of an artificial intelligentassistant according to exemplary and non-limiting embodiments.

FIGS. 21-27 illustrate an example augmented reality glasses userinterface view of an artificial intelligent assistant according toexemplary and non-limiting embodiments.

DETAILED DESCRIPTION

Effective communication requires much more than corrections to spellingand grammar. It requires attention to the audience of the communicationas well as to context surrounding the conversation. Therefore, in orderto increase the effectiveness of communications, a writing assistantneeds to incorporate an intelligent facility. The present disclosuredescribes an artificial intelligent assistant (AIA), which is able toutilize information about the participants and the surrounding contextand other environments to generate more effective communications. TheAIA thus provides a valuable facility to users in communication amongsta diverse population.

A communication may be understood to be a communicative act betweenentities, such as between a user as a sender and a user as receiver,where there is content and context associated with the communication.Although embodiments herein describe communications between two users,such as from a user as a sender and a second user as a receiver,communications described herein should also be understood to encompassembodiments where communications are sent from a user or group oraudience, published from a user to a group or audience (e.g., wheresomething is not sent directly but published or posted online, such asincluding when it may visible to anybody but is intended and/oroptimized for a specific audience), between a group of user senders(e.g., sharing the creation of a communication) and a user receiver, andbetween multiple users and a group or audience. Users may also beadministrators or other individuals that provide services for anotheruser (e.g., administrating or aiding in communications of individuals,helping to create and maintain user profiles, and the like). Users mayalso be a computing entity, such as computing platform, a softwareagent, a bot, an online page, a webpage, and the like. Users havecertain goals, intentions and meaning that they try to convey throughcontent. A users' ability to express those in content accurately and ina way that a specific receiver or audience would understand is limited,and receivers perceive the content with distortions and can react to itin different ways (e.g., actions, inactions, responses, emotions,conclusions, and the like). Receivers' ability to recover the meaningintended by the user is limited. Content can be classified intocommunication types and can take different formats and forms. A largercontext may include the use case, user intents and goals, theenvironment (e.g., communication channel, platform, conditions, and thelike), the current state of the parties (e.g., availability, emotionalstate, or physical state, such as whether they're a hurry, in commute,on vacation, under stress, tired, happy, upset, and the like), implicitor unconscious signals (e.g., tone, pauses, and the like), priorcommunication history (e.g., previous communications in the same thread,previous threads involving the same users, and the like), knowledge ofthe parties (e.g., who they are, their schedules, and the like), generalworld knowledge (e.g., recent news, public facts, and the like), and thelike.

The AIA may have access to user information through a user communicationprofile (e.g., knowledge of the user), receiver communication profile(e.g. knowledge of the receiver(s), group profiles, and/or targetaudience profiles, where profiles may include information pertaining toprofessional vs. informal communication styles, communicationpreferences, native language, emotional characteristics, and the like),goals (e.g., knowledge of what the user wants to accomplish, and thelike), communication history (e.g., prior communication (e.g., emailthread, conversation, and the like), shared documents, current state ofuser (e.g., emotions, focused or distracted, and the like), relationshipbetween them, environment (e.g., home, office, or commute, and thelike), past communications, shared context (e.g., current news, popculture, and the like), individual context (e.g., vocabulary, emotions,and the like), modes of communication (e.g., email history, digitalhistory, or chats), survey information, and the like), reactions toprevious or similar communications, world knowledge (e.g., concepts andrelations, logic, facts, current news and events, pop culture, and thelike), modality (e.g., communications channels available, such as email,texting, voice, video, augmented reality, virtual reality, and thelike), may be utilized to modify a user's communication, either directly(e.g., making or providing explicit options for changes to acommunication) or indirectly (e.g., prompting the user for ways toimprove the communication). Although the present disclosure applies theAIA to improvements of a user's communication, the AIA may similarly beapplied to improving a receiver's communications, such as re-writingincoming communications for them in a way that they prefer orunderstand.

The AIA may help to optimize a communication for a specific receiver orgroup of receivers, assist in a real-time conversation, support multiplemodalities of language and communication, be adaptive to individualusers, learn over time based on how participants communicate, and thelike. The AIA may be continuously updated, have access and insightsabout individuals and groups that a user has never communicated with,and have the ability to perform in real-time. The AIA, with access to awide range of continuously updated data and modeled communicationbehavior, may be enabled with an automated means of determiningperception and predicting reaction of a receiver, where the system isable to learn the patterns it observes in the interactions betweenparticipants to predict how a specific communication will be perceivedand reacted to. In embodiments, prediction of perception and reaction ofa receiver may be enabled through models of language effectivenessand/or impact, as described herein, including general, group-domainspecific, and receiver-specific modeling, such as trained on a corpus ofpreviously observed communications and reactions.

The AIA may suggest changes related to communication structure,language, style, and the like, that relate to a specific receiver, groupof receivers, or broad audience. The AIA may suggest recomposing orrewriting a communication based on a goal, context, communication styleof a user, prior communication that achieved similar goals and effects,and the like, such as stored in a user communication profile. The AIAmay provide assistance during a real-time dialog, such as on a displayof a computing device while a conversation is ongoing (e.g., displayingfeedback to the user during a texting exchange, providing audioprompting (e.g., via a speaker in the user's ear), providing hapticfeedback during a conversation on a smart phone or augmented realityglasses, and the like). In embodiments, the AIA may be able to provideiterative real-time feedback that is visible to the user as a sender butnot to the receiver, taking the reaction to the previous communicationas additional input.

FIG. 2 illustrates an AIA communication system model, where a usergenerates a communication as an input to the MA, such as a writtenelectronic text (e.g., email, text message, document, and the like),voice communication (e.g., voice input to a telecommunications system),and the like. The MA receives and processes the communication input withrespect to a user communication profile, receiver communication profile,goals, context, world knowledge, and modality, and generates an outputin the form of a modified communication or feedback to the user that isdirected at optimizing the effectiveness, clarity, and correctness ofthe communication. Input and training data to the system may alsoconsider the current communication (e.g., a draft communication),revisions of communications, targets for a communication (e.g., goal,intended impact, audience, desired style, and the like), ‘lookalike’communications (e.g., similar communications between similar parties inthe past that are predictive to the current communication, and thelike), context (e.g., domain, topic, type of communication, priorcommunication history, user emotional state, and the like), user'scommunication profile (e.g., user's preferred style, user's vocabulary,user's proficiency, and the like), receiver's communication profile,relationship between users, communication constraints (e.g., time,length, medium, and the like), world knowledge, domain knowledge, shareddocuments between users, reaction to previous communication (e.g.,textual response, verbal response, video recorded body language (e.g.,posture), and the like), geography with respect to language (e.g.,different spellings, different idioms, and the like) and usage combinedwith geography to determine a language variation (e.g., U.S. vs.Australia or dialect (e.g., different regions within a country)),geography with respect to context (e.g., service representativeproviding feedback to a customer with respect to geography in order toincorporate ‘relatedness’ into a communication), information fromwearable devices (e.g., biosensors, cameras, microphones, and the like),and the like.

The AIA may utilize user communication profiles in the process ofoptimizing a communication, where communication profiles containproperties and communication preferences of users. A communicationprofile may be described as a set of characteristics, traits, features,and the like, that can be used to characterize and represent anindividual either independently or to cluster them as part of a largercohort. Communication profiles may be records consisting of at least oneproperty expressing characteristics related to communication, such aslanguage proficiency, vocabulary, style, topics, preferences, reactions,behavior, and the like. Communication profiles may change over time(e.g., with respect to vocabulary, language proficiency, triggers,interaction preferences, and the like). A communication profile of aperson can be learned and updated by analyzing their outgoingcommunication and reactions to incoming communication, by asking themquestions, having them fill in some of the profile properties, by usingcommunication profiles of similar users as an approximation, and thelike. Communication profiles of groups of users or larger audiences canbe formed by aggregating individual profiles or by getting statistics oncommunication with a certain group/audience.

Profiles may be associated with certain communication parties (e.g.,entities that can be senders or receivers). These may be individuals,clusters of individuals with similar socio-demographic or psychologicalproperties, clusters of individuals with similar communication profileproperties (e.g., similar vocabulary, topics, style, communicationpreferences, behavior, and the like), groups of connected individuals(e.g., coworkers within one team sharing a certain context), largerpopulations (e.g., children audience, non-native English speakers,clusters of individuals with similar socio-demographic or psychologicalproperties, clusters of individuals with similar communication profiles,groups of connected individuals (e.g., teams), audiences/audience types,and the like). Communication profiles may contain records. Records ofcommunication parties may include their socio-demographic andpsychological properties, descriptions and properties that define orunify a group, and the like. Individuals may belong to multipleclusters, groups, and audiences, such as native English speakers, ITprofessionals, teenagers, certain geographies, and the like. Individualsmay be connected as a graph with links denoting familiarity level andtypes of relationships. Familiarity may be initiated and updated basedon the frequency of observed communication and its tone, among othersignals. Relationship may be specified by users or inferred by the AIAfrom observed communication between them.

Profiles may include properties such as language proficiency level,native language, characteristics of the party's communication such asvocabulary (e.g., word lists with frequencies) and frequently usedphrases (e.g., word and phrase frequency counters updated on eachcommunication session processed by the system by extracting mostfrequent collocations and language patterns), other characteristics ofwriting/speech, such as style, structure (e.g., including vectorrepresentation of a sample of writing, individual language models,frequency counters on different aspects of style, structure, tone, andthe like), topics (e.g., list of topics with frequency counters updatedon each communication session processed and/or classified by thesystem), negative and positive triggers (e.g., specified by the user orinferred from observed reactions), communication behaviors andpreferences (e.g., activity hours, time to respond, communicationchannels, low-context vs. high-context, verbose vs. concise, emotionsvs. facts, format (e.g., plain-rich, short-long, bullets-prose, and thelike), and the like. Triggers may be communication elements orcharacteristics (e.g., words, phrases, errors, topics, formatting, andthe like) that an individual or members of a group consistently like ordislike. Triggers may cause strong emotions. Negative triggers carry therisk of tension or conflicts in communication.

The AIA may learn communication properties directly from a user (e.g.,through surveys, prompts to fill in properties or state preferences), byobserving and analyzing their communication, by analyzing/processingtheir reactions to incoming communications. For instance, communicationparties may inherit the properties of groups or audiences to which theybelong. Properties of more granular parties may override the equivalentproperties of (e.g., larger) groups or audiences. Communication profilesmay include reaction data, such as in a corpus of communications anddocuments (e.g., original or vector representations) and thecorresponding reactions from different communication parties (e.g., usedto train AIA to predict reactions of a receiver). Profiles may beinitialized with user input through surveys, iterative questions, byanalyzing records of prior communication, and the like. In embodiments,communication profiles of the receivers who do not have an existingcommunication profile in the system (e.g., not a registered user of AIA)may be have a communication profile generated in runtime and not storedpersistently or stored in de-identified clusters.

Data sources for communication profiles may include results ofresearching characteristics of certain populations, user surveys,explicitly stated user preference (e.g., filled-in part of the profile),observed outgoing communication (e.g., sampling aggregatedrepresentation, data extracted through analysis/inference, observedcommunication behavior (e.g., schedule, channels, and the like),observed reactions to incoming communications (e.g., responses,actions/inactions, and the like), data from biometric sensors (e.g., totrack the current state, emotional reactions to various communication,and the like), integrations with other systems holding relevant data,and the like.

Communication profiles may be taken from public and private sources,such as from databases, academic datasets, demographic information (canbe inferred by joining the different DBs), psychological profiles,author profiling, usage guidelines, relevant data sets (e.g.,psychological profiles, demographic data, social media status updates),and the like. Communication profile collections and usage may utilizevarious methods such as authorship attribution (e.g., word distributionfrom social media), clustering, deep learning (e.g., language modeling,author vectors, comparison, and the like), text classification (e.g.,with respect to gender), authorship score calculation, author profiling,user profiles (e.g., from e-commerce, social media, articles, and thelike), user embeddings (e.g., factor adaption, user content, user typeembeddings, and the like), personalized natural language processing,personality assessment, linguistic homophily, linguistic style, and thelike.

Communication profiles may include information about the user ofcommunications, such as their age, gender, race and ethnicity,professional concentration, occupation, past employment, currentemployment, workplace hierarchy and dynamics, residential geographiclocation, professional/work geographic location, current geographiclocation, geographic location of origin (e.g., home country, city,nationhood), primary and secondary communication languages, religiousheritage, religious views, political views, personal preferences, socialhierarchy dynamics, personal psychological type (e.g., neuroticism,extraversion, openness, agreeableness, conscientiousness, and the like),personal human values (e.g., self-transcendence, self-enhancement,conservation, openness-to-change, hedonism, and the like), Meyers-BriggsType Indicator (MBTI), cognitive style(s), contextual emotional states(e.g., under what conditions the individual experiences general positiveemotions, optimism, general negative emotions, depression, anxiety,anger, and the like), and the like.

Communication profiles may include an individual's lexical writingfeatures, such as lexical features at the character-level (e.g.,character n-grams (e.g., labeled char-n-grams), percent of charactersper document (e.g., ratios between upper case characters, emoticons,periods, ellipses, return characters, commas, parentheses, exclamations,colons, digits, semicolons, hyphens and quotation marks and the totalnumber of characters in a communication, and the like), at thecharacter-level (e.g., number of words, mean number of characters perword, mean number of characters per word, and the like), at thesentence-level (e.g., mean number of words per sentence, standarddeviation of words per sentence, difference between the maximum andminimum number of words per sentence), emoticons (e.g., number ofemoticons, number of emotions per word, sentence, document, and thelike), special characters (e.g., ‘$’, ‘%’, ‘@’, and the like),vocabulary richness (e.g., percentage of distinct words, type-tokenratio, hapax legomena, Yule's K, dis legomena, and the like),readability (e.g., metrics (e.g., Flesch-Kincaid metric), word lengthdistribution (e.g., word-lengths per sentence, email selection,document, and the like), sentence length distribution (e.g.,sentence-length per email selection, or document)), and the like.

Communication profiles may include an individual's syntactic features,such as parts of speech (POS) features (e.g., POS n-grams (e.g.,fine-grained and coarse-grained), relative frequency of comparative andsuperlative adjectives and adverbs, relative frequency of the presentand past tenses, phrase structure of general grammatical categories(e.g., noun-phrase, verb-phrase, and the like)), dependency features(e.g., frequency of each individual dependency relation per sentence,percent of modifier relations used per tree, frequency of adverbialdependencies (e.g., providing information on manner, direction, purpose,and the like), ratio of modal verbs with respect to the total number ofverbs, percent of verbs that appear in complex tenses referred to as“verb chains” (VCs)), tree features (e.g., tree width, tree depth,branching factor (e.g., mean number of children per level)), functionwords, filler words, stop-words, punctuation, formality, and the like.

Communication profiles may include an individual's discourse features.For instance, to obtain a discourse structure, the system may use adiscourse parser, which receives as input a raw text, divides it intoelementary discourse units (EDUs) and links them via discourse relationsthat follow a rhetorical structure. The system may then compute thefrequency of each discourse relation per EDU (e.g., dividing the numberof occurrences of each discourse relation by the number of EDUs pertext). The system may also determine the shape of the discourse trees(e.g., with respect to depth, width, branching factor, and the like),ratios of discourse markers, interjections, abbreviations, curse words,polar words (e.g., positive and negative words in polarity dictionarieswith respect to the total number of words in a text), and the like.

Communication profiles may include an individual's idiosyncraticfeatures, such as spelling errors, syntactically classified punctuation(e.g., end-of-sentence period, comma separating main and dependentclauses, comma in list, and the like), grammatical errors, (e.g.,sentence fragments, run-on sentences, subject-verb mismatch, repeatedwords, missing word errors, all-caps words, abbreviated words, letterinversions, only one of doubled letters, repeated letters, missinghyphen, wrong singular/plural, wrong tense, wrong verb form, wrongarticle, wrong preposition, repeated non-letter/non-numeric characters(e.g., ???, or !!!), and the like.

Communication profiles may include an individual's communicativestructural features, such as at the communication-level (e.g., hasgreeting, signature, URL, quoted content, body length, subject length,and the like), paragraph-level (e.g., mean number of paragraphs peremail, paragraph lengths, mean number of sentences per paragraph, meannumber of words per paragraph, and the like), in technical structure(e.g., attachments, fonts, use of images, and the like), and the like.

Communication profiles may include information about what a user maywrite, such as the results of statistical analysis of the user's content(e.g., word n-grams, word and document embeddings, termfrequency—inverse document frequency (tf-idf), acronyms, first-personpronouns, slangs, leetspeak (e.g., 133t, pwn3d, and the like), emoji,dictionaries, use of stock phrases (e.g., idiomatic expressions,metaphors, support verb constructions, names of persons, locations, andother entities, dates, domain-based technical terms (e.g., academic,legal, medical, scientific, administrative, and the like).

Communication profiles may be constantly updated, such as with respectto communications between participants, documents that the usergenerates or edits, writing style, errors encountered, previously statedgoals, business associations, preferences, and the like. Usercommunication profiles may then be used by the AIA to help modify newcommunications based on the latest information available for theuser(s).

Communication profiles may include representations of typical reactionsand interactions extracted from communications, such as informationextracted from previous responses (e.g., using sentiment analysis andother natural language tools), extracted non-verbal reactions fromspeech and/or video (e.g., tone, facial expressions, posture, and thelike), received interactions with other systems (e.g., views, clicks,reaction time, and the like), inputs explicitly reported by a user(e.g., in the setup or update of a communication profile, through asurvey, use of emoji, and the like), inputs reported by a user (e.g.,through a product survey), lookalike communications, and the like.

Communication profile data may be used in training data fortransformation processes. In addition to data associated with thecontent of communications, other factors may be considered. For example,a video stream of a receiver may be evaluated for body language during avideo-call, capturing the gesture and posture of the receiver. Forinstance, this data may be correlated to geographic location in order toaccount for different body language for different countries/cultures,where the information about geography is mapped to the posture in areaction (e.g., posture mapping to anger or acknowledgment).

In embodiments, when a communication is addressed to a plurality ofreceivers, the communication profiles for those targeted receivers maybe used individually to generate unique communications by the AIA foreach receiver, to generate a single audience targeted communication, ora combination of individual and group targeted communications. Inembodiments, the user may be able to specify how a plurality ofindividuals in a targeted audience population is to be communicatedwith, such as individually or as a group. For instance, the user maygenerate a single communication, but intends it to be transmitted to anaudience of ten individuals, where three may be targeted for individualcommunications and the rest targeted for a group communication. Theindividual communications may utilize the receiver's individualcommunication profiles to process the user's communication into uniquecommunications, and the rest of the communication profiles may be usedto generate some ‘average’ response (e.g., a weighted averaging ofreceiver characteristics from the group's communication profiles) fromthe user's communication. The AIA may enable the user to generate asingle communication that is customized per the target audience.

The AIA may utilize communication profiles to transfer meaning to a usercommunication. As illustrated in FIG. 3, a user, generating acommunication, may interact with the AIA, and the AIA may in turn accessthe user's communication profile in addition to the receiver'scommunication profile. Communication profiles may comprisecharacteristics such as writing style, speaking style, communicativeemotions employed in past communications, vocabulary usage, the user'snative language, contextual information, and the like. This informationfrom users may then be used by the AIA to predict perception related tohow the communication could be received; provide editing suggestions,composition suggestions, and coaching; assess how goals may be met; howlookalike communications (e.g., templates) may be used to compose a newcommunication; and the like. The AIA may assist the user in composingcommunications that transfer meaning to the receiver as intended. Theprocess may begin with the AIA processing and determining the user'sgoals, communication draft and key points, processing and incorporatingthe user's communication profiles, structuring a communication toachieve a goal, determining the best structure and tone to meet thegoals, and the like. For instance, the AIA may add a clarifying point toa communication based on email history and context of the receiver, uselanguage and style that relates with the receiver, change the tone of anemotionally charged communication so it's less likely to offend thereceiver, use a different word the receiver will understand, make thetext written by a non-native speaker less idiomatic, and the like.

In embodiments, the AIA may operate on a plurality of computer-basedplatforms, such as on laptop/desktop computers (e.g., nativeapplications, editor integrations, and the like), Web (e.g., standalone,browser extension, and the like), mobile computing devices (e.g., smartphone, tablet, and the like), augmented or virtual reality glasses, homeand office interactive assistant augmentation, video conferencing, voiceassistance, wearables, and the like. The AIA's ability to imbibeinformation, context, and global knowledge into a communication may beapplied to a great diversity of applications, including personal andprofessional communications (e.g. adapting to different genres, styles,formalities, and the like), individual and group communications (e.g.,targeting individuals, groups, or a combination of individuals andgroups, and the like), real-time conversational assistance (e.g., aidinga user during a live conversation), interactive communication generation(e.g., aiding the user in drafting an email or text prior to sending),entertainment (e.g., applying a humor filter to a communication),business services (e.g., modifying a communication with a client), andthe like. For example, the AIA may help a user generate a communicationthrough an interactive exchange, where the AIA interjects feedbackassociated with different target audience characteristics (e.g.,composing an email to your mother, versus to a friend or professionalassociate). In another example, the AIA may aid a user during aconversation, such as during a voice conversation or a text exchange(e.g., where the AIA provides feedback during the conversation on asmart phone through a displayed dialog box, on a pair of augmentedreality glasses, and the like). The MA may also be applied toentertainment. For example, a user may want to add a personal anecdote(e.g., to lighten the tone of the communication) or turn a benigncommunication into a funny one (e.g., applying a humor filter, such asin a similar fashion as people applying a humor filter to a photo).

The AIA may be applied to a plurality of use-cases, such as for grammarand fluency correction, clarity and effectiveness in combination withcontext and communication profiles, improving human-computer interactioneffectiveness, augmenting outgoing voice and video messages, augmentingconversations, augmenting incoming communications, providingcommunication analytics and coaching, providing conversational agentmodality, and the like.

The AIA may be able to predict the reaction of a receiver, such asalerting the user if a communication doesn't match the target receiver'spreferences and suggest revisions. Given a communication and a goal ordesired reaction (e.g., where the goal is to entertain, where the targetreaction is to produce a smile or a laugh, and the like) the system maypredict whether the communication is likely to cause this reaction,based on the corresponding component of the receiver's communicationprofile (e.g., what he or she tends to find entertaining). Then the AIAmay generate revisions that make the communication more likely to sparkthe desired reaction (i.e., make it more similar to other communicationsthat the system has seen that triggered a similar reaction from thisspecific receiver or group). Once the communication has been sent and aresponse has been received, the MA may help the user understand aparticular reaction, such as detecting tone and other non-verbal signalsin the incoming communication, generate advice on how to reinforcedesirable reaction and mitigate an undesirable reaction, and the like.The AIA may utilize or generate a communication template given the goaland context of a communication, such as based on patterns learned fromother communications with the same goal and similar context that thesystem has seen, or suggest a communication format and modality based onthe goals, context, and communication profile of the receiver, such asconverting the communication into a different modality, picking adelivery channel, and the like.

In embodiments, the AIA may improve human-computer interactioneffectiveness, such as with an API for a third-party system that allowsrewriting an automatically generated communication to look more like itwas drafted by a human or appealing to a given target audience orreceiver, and effective for a given goal. AIA may customize thecommunication for each receiver based on their communication profilesand select an appropriate delivery channel, such as based on anautomatically generated communication with context, goals, a list ofreceivers and the like.

In embodiments, the AIA may provide communication analytics andcoaching. For instance, the AIA may provide coaching based on observedcommunication, where the system generates personalized advice on whatthe user could do to improve communication and be more effective orproductive (e.g., develop active listening or non-violent communicationskills, expand vocabulary, brush up on grammar, be more concise, improvepronunciation, speak slower, use questions instead of directives, limitsocial media use, and the like), avoid common mistakes the usertypically makes (e.g., in order to gradually reduce repetitive errors orbehavior), and the like. The AIA may capture not only outgoingcommunication or conversations but also consumed information, such asusing a camera or microphone feed from a wearable device, such as onsmart glasses, analyze the receiver's reactions to various content andconversations using data from sensors, such as wearable and biosensors(e.g., temporal data, and the like), and use this to build or augmentthe receiver's communication profile and for communication coaching.Coaching may be provided in real-time, as feedback, provided off-line,and the like. In embodiments, this information may also be used astraining data for communication models.

In embodiments, the AIA may provide a facility for conversational agentmodality, where the AIA may vary the type of interaction systemutilized. For instance, a “highlight and suggest” interaction may beutilized, or the AIA may rewrite a sentence or passage given differentconstraints (e.g., goals, proofing, clarity, effectiveness, and thelike), or in a ‘more forward thinking’ mode, where the system chats withthe user to work with them interactively to improve their communication,such as a friend or tutor would. For example, a user may generate amessage, and receive an interactive dialog, such as “Hi! I've read yourcommunication and have some tips! Your grammar is fine, but I amconcerned about organization. Can we start?” Once the user agrees, theAIA may provide guidance and dialog, such as “I've highlighted what Ibelieve is the thesis statement of your message. I think this would makemore sense moved up. Let me show you.”

The AIA may provide a host of messaging improvement facilities, whereany number of them may be utilized and combined. For example, a user mayutilize the AIA to write an email for applying for a job. The person maywrite a first pass email and then the AIA chooses the best modality togive them feedback. Maybe the user is a proficient writer and can lookat comments made on the side, maybe the user is embarking on their firstjob search and the AIA may select a “conversational agent” mode and walkthe user through why certain things should be changed and what is good,and why. With a modality selected, such as for “automatic rewrite”, AIAmay review the draft, use language modules to check for grammaticalerrors, recognize that the goal of the email is to impress the receiver(e.g., a hiring manager), and elect to transform the text into a moreformal style. The AIA may access receiver communication profiles anddetermine that the receiver likes a certain pop culture reference andlikes attachments in PDFs, so a subtle reference to a popular movie isslipped into the cover letter and the cover letter is converted from aword processing format into a PDF format. In addition, given the goal,the AIA may make sure to open the email with some ebullience to showthat the user is really interested in the job and pulls content fromtheir cover letter that would back up their enthusiasm for the job. Forexample, if they were applying for a job in a particular company, thesystem may pull out an internship at that company from last summer anduse that as an example. The system may also check for missinginformation, such as a signature at the bottom of the email and phonenumber where they can be reached. In this interchange, the AIA mayutilize a number of communication improvement facilities in combination,such as modality selection, proofreading, effectiveness, styletransformation (e.g., includes vocabulary shifting), goal detection andoptimization, receiver communication profile scanning (e.g., inclusionof pop culture reference, affinity for a certain file format, and thelike), effectiveness (e.g., some parts are made more persuasive to showinterest), argumentation (e.g., supported by pulling facts fromdifferent sources automatically), check for missing information (e.g.,system has an idea of what a good letter looks like for this task), andthe like.

In embodiments, the AIA may be utilized in an educational environment,such as where a user is a student that is provided suggestions withrespect to writing structure and style, with prompts to inappropriatetone, with additional context associated with new concepts, and thelike. For instance, a student may be writing a paper on the civil war,and the AIA not only suggests modifications to the text with respect toimproving the student's quality of writing, but also suggests context toaid in the support for a student's theme or bring related current eventsto the student's attention.

In embodiments, the AIA may be used for grammar and fluency correction.For instance, the AIA may utilize context to improve grammar andfluency, such as applying a different dictionary and grammar based on auser's location, the specified audience (e.g., use Australian Englishspelling and grammar norms by default if the users are (mostly)Australian), and the like. The AIA may utilize communication profiles toimprove grammar and fluency, such as applying a different grammaticalerror correction model depending on the user's native language (e.g.,applying a model trained on text with errors typical for Chineselearners of English to improve the accuracy of correction), adjustingspelling and grammar correction according to a receiver's sensitivity oropinion on certain writing issues. For example, based on an observedcommunication profile of the receiver, the system may know that he orshe gets annoyed by lowercasing in short text messages or by sentencesending with prepositions, and enable the corresponding checks andapplications of corrections if a user is writing to this person.

In embodiments, the AIA may be used to improve clarity andeffectiveness, such as through context and communication profiles. Forinstance, an alert of readability or vocabulary mismatch may be providedto a user based on the target audience (e.g., too many idioms in a textfor non-native speakers or inclusion of complex language in a text forchildren), a suggestion for an improvement to or automatic rewrite ofthe text may be provided to adjust readability and vocabulary, a rewriteof an email may be provided to maximize a positive outcome, and thelike. For a given email, the AIA may suggest or apply the structure andrevisions that increase the chance of a positive response or rewrite thetext to make it more effective (e.g., targeting specific sentences orgenerating a new email from scratch). A communication profile, priorcommunication, documents, calendar information, and the like, may betaken into account, such as were shared in the thread or elsewhere. Forexample, a sales email may be rewritten using a model trained on acorpus of similar sales emails with response and/or click data. Inanother example, improvements or a rewrite may be suggested to a supportemail so that a specific customer (receiver) is satisfied, such as usinga model that is trained on a corpus of similar support emails taggedwith satisfaction ratings, and possibly also the receiver'scommunication profile.

The AIA may have the capability to modify a communication to appeal to aspecific receiver, such as generating revisions or rewriting thecommunication using the vocabulary that the receiver would understand orusing the structure and language that the receiver tends to use orreacts positively to (e.g., based on his or her communication profile).Adding or removing content may be suggested, such as depending on theuser's goals and receiver's background, such as using individual andshared context available to the system, or adding references and contentfrom documents (e.g., previous interactions with the receiver), popculture, current news or other kind of world knowledge.

The AIA may have the capability to detect biased or inflammatorylanguage, and suggest more balanced rewrites when possible, such aswarning the user if a specific receiver can be annoyed by certain formsof language (e.g., structure, tone, certain terms, and the like), basedon his or her communication profile. The AIA may detect a user'semotional or physiological state to correlate with their current writingstyle, such as through biometric data from the user (e.g., from awearable device), visual indicators (e.g., from facial indicatorsanalyzed from a user-facing camera, such as mounted on a user's laptopor smartphone), changes in writing style with respect past messaging andresponses, and the like. For instance, a user may be wearing a smartwatch that monitors temperature, heart rate, galvanic skin response,voice data (e.g., vocal inflections, volume, speech patterns, and thelike), and blood sugar, where changes in these levels are matched toemotional profile data, such as stored on the user's communicationprofile, as determined from similar measurements in a population, andthe like. The AIA may then be able to determine if these indicators areadversely affecting the user's communications, such as when a spike inbody temperature, heart rate and low blood sugar are detected, making aperson more likely to communicate in a frustrated or angry manner. TheAIA may then prompt the user to be aware of such potential influences,offer communication suggestions related to same or revise acommunication. The AIA may also correlate detected emotions with thecontent and/or communication the user deals with and use this astraining data for modules that predict reactions and also for updatingcommunication profiles.

In embodiments, communication profiles and available context may beutilized to compose and/or edit user content (e.g., an electroniccommunication) in a way that maximizes the probability of thereceiver(s) perceiving the received content as intended by the user andreacting positively. FIG. 4 depicts a workflow of asynchronouscommunication, including general advice to a user generating acommunication, and compositional and editing assistance to the user forthe communication. The workflow begins with the user 404 (e.g., a usersender) providing a communication input 402 to the AIA 400, where theAIA receives the communication input 402 with a content and contextmodule 414. The user 404 may provide an initial communication input 402(e.g. draft a message, upload a document, or just start typing ordictating). The user 404 may provide context by specifying the use case,goals, intent, desired reaction, and the like. The user 404 may provideprior communication history by opening a thread and/or granting AIAaccess to their inbox. AIA may infer more context from the communicationenvironment, integrated systems, recent user activity (e.g., channel,schedule, user's current state, and the like). AIA may classify thecommunication type and use case based on initial content and/or priorhistory. Techniques for classification may include rule-based, machinelearning, deep learning, statistical models, and the like. MA maypresent (at least in part) inferred context to the user 404 to confirmor override. The AIA may apply default context values (e.g., defaultgoals for a certain use case) if they are not provided by the user 704and cannot be reliably inferred.

The user 404 may specify a second user 412 (e.g., a user receiver), suchas by selecting from a contact list or picking applicable audiencetypes, as described herein. The AIA may identify the use case throughinteraction with the user 404, such as the goals of communication anddesired/undesired reactions. The AIA may begin the session aspre-configured or be launched with a certain use case parameter, inferthe use case from the environment or prior communication, prompt theuser 404 to specify the use case (e.g., “feedback”, “personal request”,“confirmation”, “greeting”, “formal application”, “report”, and thelike). Based on the use case, the AIA may infer the goals andcommunication objectives and prompt the user 404 to confirm or modifythem (e.g., “convince”, “impress”, “entertain”, “inform”, “expressgratitude”, and the like). Based on the use case and goals, the AIA mayidentify or prompt the user 404 to specify desired and undesiredreactions of the user 412 (e.g., emotions, actions, and the like). Forinstance, a default desired reaction could be “a (positive) response”. Adefault undesired reaction could be “no response”, “negative response”,“anger”, “frustration”, and the like. The MA may provide for a defaultset of messaging goals, such as that a communication should be accurate(e.g., error-free), not ambiguous (e.g., clear), not harmful oroffensive, doesn't cause or escalate conflict, minimizes the risk ofmisunderstanding, and the like. In embodiments, the goals ofcommunication and desired/undesired reactions may be updated at any timethroughout the interaction with the user, as described herein, based onupdated inputs (e.g., as the user 404 enters the subject for thecommunication or proceeds to type or dictate the communication).

The AIA may identify other communication context, such as acommunication channel, platform, or medium (e.g., web, mobile, specificcommunication clients and collaborative editors, chat, text/voice, andthe like), a communication type, form, genre (e.g., email thread,short/instant message, shared document, social media post/comment, voicemessage, and the like), prior communications between the user 404 anduser 412, location and time of day/week for the user(s), and the like.The AIA may identify that the channel, time or the form of thecommunication are not optimal given the receiver and the use case/goalsand suggest alternatives, such as based on the preferences explicitlyprovided by the user 412 or derived from receiver's previouscommunication patterns observed and learned by the AIA, such asstatistics of the receiver's use of channels for different use cases,availability or schedule (e.g., extracted from receiver's calendar),typical time they're checking and responding to incoming communication,communication forms they tend to respond to better, and the like. Thisrecommendation may be updated as the user 404 works on the communication(e.g., suggest an email or document form instead of an instant messageif it exceeds certain size, suggest a short video recording if thecommunication is more informal). Extracted content and context obtainedby the content and context module 414 is then passed to thepreprocessing module 416, which encodes it into a common representation(e.g., as a vector or matrix) to be used as an input intomachine-learning models, deep-learning models, other statistical modes,and the like, within the communication transformation module 418.

The preprocessing module 416 prepares content and context, such as inassociation with accessed communication profiles 428 (e.g., users and/orgroup profiles), for use by the communication transformation module 418.In embodiments, the AIA 400 may interface with third-party communicationplatforms (or may be integrated with the third-party communicationplatform) to retrieve information about the user 412, such as a receiverID (e.g., email address) derived through extraction from the receiverfield, from the thread or conversation (if it's a response), and thelike, from the user's communication input 402. For example, accessingthe receiver field through a Gmail API or browser extension.Alternatively, the AIA may prompt the user 404 to specify thereceiver/audience, such as by entering an email, selecting a contactfrom the list, selecting the audience or group from the list (e.g., teamX, family members, industry group Y), selecting the receiver or audiencetype from the list (e.g., a close friend, a boss, expert audience,children audience, or native/non-native speakers), and the like. The AIAmay infer the receiver type or target group or audience based on theenvironment or context of the communication input 402, such as thecommunication client, user account, prior content of the thread orconversation, communication content, and the like, where for instance,the automatically identified receiver type or audience is presented tothe user. The user 402 may be presented this information, and beprovided an option to override, specify, narrow down, and the like, theselection. If the user 412 is known to the AIA, the preprocessing module416 pulls the corresponding communication profile 428. If not, it maycreate a new one. The preprocessing module 416 may also associate theuser 412 with relevant group communication profiles 428. In embodiments,communication profile creation and visibility may be restricted tomembers of a certain group (e.g., members of a corporate email domain,contacts specified by the user, and the like).

The preprocessing module 416 passes the encoded content and context, andthe information accessed in communication profiles 428, to thecommunication transformation module 418 for optional processing,including general advice to the user through the advice module 420,communication composition assistance through the composition module 422,and editing of communication versions through the editing module 424,such as in conjunction with communication sources 430 as describedherein. For instance, as part of providing general advice through theadvice module, once the communication profile 428 is identified, AIA maydisplay general characteristics of the receiver or audience and providerecommendations on how to communicate with them. Characteristics mayinclude receiver's occupation or role, location, knowledge and interestareas, receiver's language proficiency (e.g., native or non-nativeEnglish speaker), expected or average response time, availability,personality type or traits, and the like. Characteristics may beexplicitly provided to AIA by the receiver (e.g., as part of filling inAIA user profile), learned through surveys (e.g., personality type ortraits can be learned through a personality survey delivered to thereceiver by AIA), which may be in-product surveys, obtained fromexternal systems accessible to AIA (e.g., receiver's occupation may beextracted from a public personal information directory, professionalcontact network, web search and the like; receiver's availability may beobtained from a calendar) or extracted by AIA from receiver's observedcommunication (e.g., receiver's knowledge and interests can be learnedby analyzing the receiver's communication through topic classificationor keyword extraction algorithms and identifying the most frequenttopics or keywords; receiver's language proficiency can be identified byanalyzing receiver's vocabulary or running receiver's communicationthrough a statistical learning algorithm classifying input communicationas native or non-native (produced by a native or a non-native speaker);expected response time may be based on historic data on receiver'stypical response time in the same or similar communication scenarios).Recommendations may include preferences and other information explicitlyprovided by the receiver such as preferred communication channels (suchas email, instant messages, voice messages, specific communicationplatforms or applications), preferred communication format and style(such as formal or informal, brief or detailed, plain or rich text,logical or emotional, and the like), preferred communication time (e.g.,separately for different communication use cases, such as personal andbusiness) and the like. Recommendations may also be derived by AIAthrough analyzing a receiver's communication patterns (e.g., AIA mayrecommend formats and styles that the receiver responds to mostpositively and consistently (based on the sentiment analysis ofresponses and the distribution of time to respond); alternatively, AIAmay recommend formats and styles that match/mimic the receiver'sprevalent communication styles and format).

As a part of general advice, the MA may suggest a different time to senda communication, such as if the receiver is determined to be occupied(e.g., based on a retrieved schedule if the AIA has access to it), ifthe receiver is more likely to provide sufficient attention at thecurrent time where the receiver may better respond to an incomingcommunication sent at a different time (e.g., based on their statedpreferences or past behavior recorded in their communication profile,such as when the receiver most frequently checks and responds toincoming communication). Suggested time may be different for differentcommunication use cases, such as personal and business (e.g., ifreceiver's explicit preferences or observed behavior differ in these usecases). The AIA may suggest a different channel to send a communication(e.g., email, instant messages, voice messages, specific communicationplatforms or applications), such as if the receiver is likely to see itand respond faster through a different channel (based on their statedpreferences or past behavior recorded in their communication profile,such as the frequency of use of different channels for differentcommunication use cases (among the channels AIA integrates with or hasaccess to)), if the topic, structure, or length of the communication area better fit for a different communication channel (e.g., sensitivetopics may better be discussed over phone or longer communications maybetter be sent over email rather than IM), and the like.

As part of general advice, given a message produced by the user, the AIAmay inform the user of predicted receiver's reaction to such message.AIA may alert the user in case the predicted reaction differssignificantly from a desired/expected reaction. The desired/expectedreaction may be derived from the user's intent or pre-defined in the MAfor certain communication use cases (e.g., if the user's intent is“persuade”, MA may consider “response with a confirmation” asdesired/expected reaction; if the use case is a social media post, AIAmay have likes, shares and comments pre-defined as desired/expectedreactions).

In embodiments, the AIA may provide for advice to a user who has sent acommunication with feedback from the receiver of the communication, suchas described herein. For instance, a user may be provided with reactionfeedback, where the AIA provides advice on how to follow up with asecond communication to the receiver, such as changing the tone of thecommunication, adding more information, providing less information,making the communication less formal, removing language identified asundesirable, abusive, or offensive by the feedback, and the like. Thefeedback may also be included in an update to the user and/or thereceiver's profile, so that any preferences or issues raised in thefeedback can be incorporated into future communications.

The communication transformation module 418 may provide interactivefeedback 706, such as to prompt the user to specify a user/audience andthe context that AIA cannot get or infer from the environment, prioruser input, user settings, available communication draft and history,and the like. The AIA may prompt the user to confirm or override thereceiver/audience and the context the AIA has identified. The user mayupdate the content, context, and receivers at any time during thesession. The AIA may detect changes, re-process inputs and adjust itsfeedback; present general recommendations that the user can react to(e.g., acknowledge, dismiss, mark as helpful or incorrect, and thelike); use user feedback on the general recommendations to improve themin the future; present template and/or textual completion options (e.g.,the user may preview and select one or none of them); offer completionsand continuously update completion suggestions as the user is enteringtext (e.g., typing or dictating); and the like. In more structuredcommunication use cases, the AIA may guide the user through a singletemplate/outline and have the user answer questions/fill in certainblocks of content. The AIA may generate the edits for the text the userhas entered and automatically re-check it and update the edits when theuser modifies the text, rewrite the content and highlight changes andallow the user to review and revert individual ones, and the like. Theuser may interact with and provide feedback on AIA's individualsuggestions (e.g., general recommendations, templates, completions,edits, and the like). The AIA may use the data from user interactions(e.g., accepted or dismissed suggestions) and feedback (e.g., incorrector irrelevant suggestion reports) to improve future suggestions (e.g.,by using this data to re-train/tune the corresponding machine, deep, orstatistical learning models).

As further depicted in FIG. 5, the composition module 422 helps the user404 compose communications with less effort and with greater speed, suchas by generating (at least a portion of) communication content (e.g.,text/language) from a limited communication input 402, guiding the userby suggesting structure and/or placeholders to fill in content, and thelike. It may also ensure the quality and effectiveness of communicationby optimizing the generated language and structure for specific contextand communication receivers. The composition module 422 may implementpredictive language generation based on initial communication input 402and available context, use case, and the like. It may suggestcommunication templates, generate textual completions, such aspredicting the next word, phrase, sentence, paragraph or an entiremessage/document/communication. It may present options for the user toconfirm or select from. It may optimize generated language to a specificreceiver(s)/audience, and/or personalize it to mimic the user'scommunication style. The Composition module 422 may use communicationtemplates 504 for various communication scenarios, statisticalalgorithms for generating textual predictions (e.g., machine learning,deep learning, language models, and the like), and other (prospective)forms of natural language generation. Language generation by thecomposition module 422 may optimize compositional suggestions to theuser for utility and impact (effectiveness). Utility optimization can beachieved by maximizing the probability of the user 404 accepting thesystem generated text (or one of the suggestions if multiple options arepresented), and penalize compositional selections for inapplicable,irrelevant, or too general suggestions (e.g., penalizing templates withhigher percent of placeholders vs. actual text, penalizing suggestionsthat apply to a broad range of use cases vs. the actual use case, andthe like). Impact and effectiveness optimization with respect to theuser's communication goals can be achieved by maximizing the probabilityof a positive outcome or reaction by the receiver given the context, usecase, and the like, for the communication. The probability of a positive(desired) reaction may be estimated by a machine, deep, or otherstatistical learning model trained on a large set of reaction data(records of past communication content with meta-data such as context,senders, receivers/audience and the actual reactions). Variations ofcontent generated by the composition module may be run through thereaction prediction model and filtered or ranked based on the predictedprobability of desired reaction before being presented to the user.

The system may accumulate a great amount of content (e.g., messages,documents, and the like) with meta-data: context (e.g., a resume for anengineering position at a startup, or a marketing email in China) andsenders/recipients (e.g., identifiers, emails or references tocommunication profiles). Receivers may be an audience (e.g., ITrecruiters, consumers in China). This data may be one general-purposedata set mixing everything or multiple specialized datasets, such as usecase-specific (e.g., only marketing emails) or receiver-specific (e.g.,email history of a specific user). Meta-data may also include the actualreactions (e.g., the number of interview invitations for each resume,the number of clicks or product purchases triggered by each marketingemail, the sentiment of a response to each user email, and the like).The system may train machine learning, deep learning, or otherstatistical models on this data and use these models in reactionprediction/optimization module of MA. The system may have a model thatpredicts the reactions to a given user's message, context, and thereceiver or audience (e.g., returns the probability of differentreactions. The system may also have a model that rewrites a given user'smessage to optimize for a desired reaction. It may take a draft of themessage, context (including the desired reaction), and the receiver asan input and produce a rewrite that maximizes the probability of thedesired reaction. These models may be general-purpose (e.g., trained ongeneral-purpose data set) or specialized (trained on specializeddatasets). Afterwards, the AIA may capture the actual reaction to theuser's communication, construct a reaction data record and feed it intothe model(s) to update/retrain them (e.g., online learning).

Communication templates 504 may be especially applicable in frequent orrepeated communication scenarios, such as short requests or replies,common communication types (e.g., introductions, greetings, invitations,resumes, applications, or reports) and the like. Communication templates504 may not be as applicable for creative writing, describing personalexperience, communications containing complex ideas or argumentation,and the like. The composition module 422 may select communicationtemplates 804 from clusters of semantically close or equivalenttemplates for different communication use cases. Communication templates422 may consist of text and placeholders, or a combination thereof. Somemay be ready for immediate use (e.g., short requests and replies), somemay be a sequence of placeholders, or an outline defining structural andlogical blocks that the user would have to flesh out. Communicationtemplates may include short communications (e.g., requests, replies,greetings, and the like), use-case specific templates (e.g., separatefor different channels, communication types, and other contextcategories/conditions; clusters of variations catering to differentcommunication profiles, and the like). User-specific or group-specificcommunication templates may be frequently used by the user or by a groupthat the user belongs to or is targeting (e.g., team- orcompany-specific templates). Communication templates 504 may contain amix of manually created and automatically generated templates. Templatesmay be manually created either by the users (e.g., administratorsproviding organization-specific templates for all users in theorganization) or by AIA developers/maintainers (e.g., a database ofprofessionally edited templates for various common communicationscenarios/use cases). Some communication templates 804 may be generatedautomatically by processing a large body of documents and messages,normalizing and canonicalizing them using different kinds of parsing,chunking or text similarity techniques, and then clustering equivalentones (e.g., with semi-supervised machine learning). In embodiments, ahybrid approach to template curation may also be utilized, where peoplemanually verify/edit automatically generated templates before makingthem available.

In embodiments, selection of communication templates 504 may beimplemented as a machine, deep, or other statistical learningmodel/technique that finds the most likely templates given the initialcontent, such as previous communications in the communication thread,and known context, such as a user's intent. The template selector module806 identifies a subset of communication templates to consider, such asbased on available context, (e.g., communication type, receivers, e.g.,team or organization) and applies a machine learning model to scorethese templates and find the highest scoring one(s). As apost-processing step, the template selector may discard or penalizecommunication templates 504 that are too general and ensure diversity ina resulting set of communication template candidates, such as forfurther personalization and targeting and also to provide the user 404 achoice.

In embodiments, automatically generating textual completions 508 may beimplemented using machine, deep, or other statistical learning modelsconditioned on the prefix word sequence (e.g., current communicationdraft), n previous communication(s) in the thread/conversation,additional context, and the like. They may generate the next word,phrase, sentence, paragraph, or an entire message or document. Inembodiments, representations of the previous content and context are fedinto a natural language generation model that is trained on large-scalecommunication data that may be mixed with users' prior communicationand/or effective communications of other people similar to the user(e.g., with respect to industry group, and the like), and/or effectiveexamples of communication in the same or similar scenario (e.g., withrespect to communication type, use case, and the like) such as giving ahigher weight to the mixed-in data. The outputs of the model are rankedbased on the receiver's communication profile and other contextualinformation. Output candidates can be ranked by the probability of themtriggering desired reaction or outcome, or based on their similarity tothe user's communication style. The highest-ranking outputs aresuggested for the user to confirm/select. The user may confirm/select acompletion to use as-is or modify.

The composition module 422 may utilize an automatic content fillfunction 512 in association with the appropriate placeholders utilizingthe language generation 510 facility, such as with filling in personaluser data (e.g., full name, contact details, addresses, and other datathat the user may frequently share in communication), public facts orworld knowledge (e.g., filling in the full date when only a day of weekis mentioned, filling in the public addresses of meeting placesmentioned), and the like. It may also prompt the user to fill in certainplaceholders in communication templates suggested by AIA.

The personalization and targeting facility 514, such as in conjunctionwith targeted and personalized content suggestions 518, may be able toprovide personalization and targeting of new content, and filtergenerated language options (e.g., select variations from a cluster oftemplates or textual completion candidates) based on the user'scommunication profile (e.g., to match their communication style) orbased on the receiver's communication profile (pick the ones that mayappeal more to the receiver to increase the likelihood of a positivereaction). The personalization and targeting facility 514 may providefor reaction prediction 516, such as in conjunction with reaction datastored in communication profiles 428. The personalization and targetingfacility 814 may also dynamically modify selected templates, such as byadjusting vocabulary and/or style to mimic the user's communicationand/or optimize for a specific receiver or audience. The personalizationand targeting facility may use a style transfer module implemented witha machine, deep, or other statistical learning model or models, such asa sequence to sequence model or models that translate generated languagevariations from generic style into the user's authentic communicationstyle. The personalization and targeting facility may use a reactionprediction module implemented with a machine, deep, or other statisticallearning model trained on reaction data (communication records enrichedwith meta-data including reactions of specific receivers) to estimatethe probability of a positive reaction and filter or rank generatedlanguage variations based on that. Language generation models may alsobe trained on reaction-enriched data to generate variations thatmaximize the probability of positive reaction in the first place.

As further depicted if FIG. 6, the editing module 424 analyzes andmodifies an existing fragment or the entire communication to optimize itfor a specific receiver(s) or audience and use case. The editing module424 helps the user 404 ensure the quality of their communication byanalyzing existing language and generating changes that improve itsaccuracy, clarity, and effectiveness with respect to communicationcontext and communication profiles. The editing module 424 may includefeatures (checks) that analyze and improve different aspects ofcommunication quality. At least some of these checks may be context- andcommunication party-dependent. Checks may utilize a rule-based andstatistical module 602, a machine learning model(s) 602, deep learningbased models, and the like, or hybrid of different processing modulefunctionality. The editing module 424 may generate modifications andpresent them to the user 404 to review and confirm or rejectindividually. It may also modify existing language and present (mark ornotify of) the changes it has made—and the user may be able to revertindividual changes.

The editing module 424 may provide for context- and profile-dependentchecks, such as with respect to accuracy, clarity, effectiveness, andthe like. Accuracy checks may include spelling and grammar correction byapplying the norms of a specific language dialect, such based on thereceiver's dialect, spelling, and grammar correction applied todifferent parameters or models. Accuracy checks may depend on the user'scommunication profile (e.g., depending on the user's first languageand/or language proficiency), and adjust a model's parameters, such asusing a model trained on a corpus of texts by the people with same firstlanguage/proficiency, resulting in an optimized check for a specificerror distribution and frequency. Clarity and effectiveness may includepreparing the communication in a way the receiver can listen to (e.g.,without shutting down, and building trust), such as by optimizingtrigger words (e.g., removing or replacing negative trigger words,adding more positive trigger words, and the like, such as describedherein), prompting the user to add positive feedback that the receiverwould appreciate (depending on the use case), replacing non-inclusivelanguage that may make the receiver feel excluded, flagging aggressivelanguage (e.g., suggesting the user to wait for a later time to send thecommunication), applying the style preferred by the receiver, e.g.,formal vs. informal, brief vs. detailed, and the like. Clarity andeffectiveness may include preparing the communication in a way thereceiver can understand (e.g., building clarity), such as by improvingthe communication for readability/clarity (e.g., adjusting readability(sentence and word complexity) and vocabulary to the receiver's languageproficiency level (including native/non-native status), adjusting forformatting based on receiver's preferences, predicting reaction of thereceiver (e.g., in the case of direct communication) or audience (incase of a broadcast communication), warning the user when a negative orundesired reaction is likely (e.g., highlighting content language thatcan cause negative emotions for a specific receiver or group,highlighting content that may not engage the receiver (e.g., too long,irrelevant, and the like), warning when it's not a good time to contactthe receiver (e.g., they are unavailable, busy, sick, stressed, such asbased on the recent communication history, calendar integration,explicit receiver settings, observed receiver state, and the like),modifying communication to increase the likelihood of a positivereaction (e.g., through recommendations, suggesting changes, or acomplete rewrite)), and the like. The AIA may use reaction predictionsimilar to the one provided in the composition module 422 to rank andfilter to generate edits. For instance, if predicted reactions do notmatch desired reactions and/or match undesired reactions, the AIA maywarn the user and suggest modifications to the draft, for example tohighlight suboptimal fragments (e.g., identified by the reactionprediction model), offer vocabulary suggestions (e.g., wordreplacements) that maximize the likelihood of a desired reaction, offerstructural changes (e.g., size, outline, transitions, paragraphsplitting, and the like) that maximize the probability of desiredreaction, offer a complete rewrite of the draft, and the like. Inembodiments, the AIA may make corrections to the communicationautomatically, without user intervention.

The AIA 400 may provide for a reaction capture module 726 to capture thereceiver's reaction 410 in order to improve the effectiveness ofgenerated or modified communications using reaction data, such asthrough updating communication profiles, collecting reaction data fortraining (in batch or online mode) the models that predict reactions andoptimize communication for certain reactions, and the like. The AIA'sreaction capture module 426 may capture reactions 410 through acommunication platform (e.g., responses, time to respond, sentiment ofresponses, and the like) or integrations with other systems (e.g., linkclicks, or other receiver actions triggered by the communication).Reaction capture module 426 may capture reactions 410 directly from thereceiver (e.g., if the receiver is also an AIA user), ask the user 704to report the receiver reaction 410 as the outcome of a certaincommunication, and the like. Receiver reaction data may also be obtainedand processed in a batch outside of the AIA workflow (e.g., extractedthrough an integration or an API from an external system, from anarchive of prior communication, and the like).

The AIA may include a reaction prediction and optimization facility thatmay utilize reaction data, such as consisting of records ofcommunication acts (cnt, p, cxt, ro), where cnt is content (e.g.,message, document, text, audio, video, in raw form or included meta datasuch as for formatting, markup, annotations, tags, and the like), p=(s,r) are references/identifiers of communication parties, where s is auser sender and r is a user receiver/audience (e.g., r may be anindividual or a group or a category of receivers, s may be a human or acomputational system capable of generating natural language, s,r mayinclude properties and characteristics of the parties or link tocommunication profiles of the parties, r may include the type ofrelationship between the recipient(s) and the sender, cxt is context(e.g., may include the use case, user goals, references to communicationhistory (such as previous communications in a thread) or relateddocuments, current psycho-emotional state of communication parties,their availability, location, and the like), ro is a reaction or outcomethat the content cnt triggered with the receiver(s) r. Reaction/outcomero may be a response or no response, time to response, number ofsubsequent communications in the thread (e.g., before the goal ofcommunication was achieved), sentiment or type of the response(positive/negative, confirmation/rejection), receiver emotions, usecase-specific response (e.g., containing certain elements/words),receiver actions (e.g., clicks on the link in the communication,signups), additions or deletions of participants in a thread orconversation, and the like. In case r is plural (a group/audience), romay be an aggregate, such as the total number of receiver actions,positive and negative responses, clicks, and the like. Response data maybe one or multiple data sets (e.g., use case or receiver-specific).Reaction data may be used to train a machine, deep, or other statisticlearning model that would predict the most likely reaction-outcome rofor a given (cnt, p, cxt).

Referring to FIG. 7, this reaction prediction 516 may be used to informthe user sender 404 of the likely reaction-outcome, such as from theuser sender 404 send content 702 to a user receiver 412. The userreceiver 412 then produces a reaction 704 which is then captured 706.The captured reaction 706 may produce reaction data 708 to be stored, aswell as being provided for reaction predication 516. Referring to FIG.8, the reaction prediction may be used to rank different variations 806of content cnt given a desirable/acceptable set of reactions/outcomes,and the like. In embodiments, the reaction predication 516 may interactwith a reaction module consumer, such as for language generation and orsender output processing. Referring to FIG. 9, reaction data may also beused to train a machine learning model that generates or modifiescontent in a way that maximizes the probability of a certain (set of)ro. For instance, given the communication parties p, context cxt, targetreaction(s) ro, and the space of possible content variations CNT, themodel could find:cnt*=argmax P(cnt_(i)|p,cxt,ro),cnt_(i)ϵCNT

The models may be either general-purpose or context-, receiver- orreaction-specific. E.g., context-specific models may be trained onreaction data sets consisting of records of communication acts in acertain use case. Receiver-specific models may be trained oncommunication addressed to a specific receiver or audience andrepresenting their reactions. The models may be used in AI-assistedcomposition, editing, or communication analysis applications. The modelsmay be trained offline (e.g., batch learning using large reaction datasets) or online (AIA's reaction capture module may feed new reactiondata records into existing models to improve their accuracy). The modelsmay run on the server side (e.g., in the cloud) or may be embedded oncommunication devices.

In embodiments, the AIA may provide a receiver with a modified versionof a received communication, such as based on the communication profileof the user and the preferences of the receiver, such as describedherein. The receiver may benefit from such modification if the user hascharacteristics and behaviors that causes the communication to benon-optimal in some way, such as because the user tends to communicatein an emotional manner, the user has a language characteristic thatmakes understanding difficult (e.g., the user is a non-native speaker,uses idioms that are unfamiliar to the receiver, uses slang that isoffensive to the receiver), and the like. The AIA may monitorcommunications being received, and flag those that may needmodification, where, such as based on a user's preferences, thecommunication may be modified before it is presented to the receiver. Inembodiments, the AIA may indicate that the communication has beenmodified, indicate what portions of the communication has been modified,show the modifications in an annotated version of the originalcommunication, present both a clean modified version and an annotatedversion of the original communication, and the like. Based on pastcommunications from a user, and past reactions and preferences by thereceiver, the AIA may implement modifications to a communication withoutfirst notifying the receiver. Alternately, the receiver may be notifiedthat a modified version is available, where the receiver may be offeredoptions for reading or listening to the communication. In embodiments,the AIA may implement modifications based primarily on the receiver'scommunication profile, such as regardless of the user/source of thecommunication (e.g., modify/augment anything the receiver reads/hears,including incoming communication, online or printed content, and thelike). Based on the receiver's language proficiency, the AIA may adjustreadability (e.g., simplify vocabulary, rephrase idioms, and the like).Based on the receiver's environment, preferences, available time, andthe like, the AIA may condense/summarize content. If the AIA knows thereceiver is sensitive to specific content (e.g., based on his/hercommunication profile), the AIA may alert the receiver or mask thiscontent. In the process, the AIA may observe and collect receiverreactions to various types of content as training data, learn from theirfeedback and interactions with its functionality that augments thecontent receivers consume, and the like.

In embodiments, the AIA may augment an incoming communication to areceiver, such as through a user interface, for transforming incomingcommunications (e.g., emails, instant message, and the like), shareddocuments, or other content that receivers consume, into a moredigestible form. Transformations of incoming communications may includeremoving wordy or redundant language that does not carry meaning so thatreading takes less time and effort; such transformations can take theform of classification machine or deep learning algorithms to strike outwords or phrases or sequence to sequence algorithms which automaticallyconvert one text to another (e.g., neural machine translation orparaphrase). Data employed ranges may be transformed from manually orautomatically curated lists of commonly overused phrases (eithergenerally, by domain or by communication party) to parallel data setswhich consist of original wordy sentences or documents and their lesswordy rewrites.

Transformations may provide summarizing longer messages or documents forquick preview. Possible applications include, and are not limited to,providing a summary or set of key points for long emails, messages, ordocuments, or automatically generating more concise versions of longpassages to make it easier for the reader to understand, and the like.Methods may include machine and deep learning techniques which canleverage parallel corpora of long sentences or passages with theirsummarized rewrites, as well as leveraging vast amounts of unlabeleddata.

In an example embodiment, and referring to FIG. 10, a communicationplatform or application 1002 may receive an original incomingcommunication with meta-data 1004, which may be classified with aclassification module 1006 and evaluated for triggers with anoptimization trigger module 1008, which my take input data from storeduser preferences and communication profiles 1010. A transform module mayprovide a plurality of transformations, as described herein, to producean optimized incoming communication.

Transformations may include formatting texts and adding structure, suchas highlighting key phrases, splitting long paragraphs, converting listsinto bullet points. Techniques may include using machine learning ordeep learning or other statistical techniques and treating the problemas a classification or sequence labeling task.

Transformations may include rewriting messages or documents usingvocabulary and readability (e.g., splitting long sentences; replacinglong or rare words with shorter, more common synonyms; replacing idiomswith literal/universally understandable equivalent phrases, and thelike) to a level that the user would understand, depending on theirlanguage proficiency level and background. Methods may include usingmanually and/or automatically curated dictionaries and reference sourcesas part of rule or statistical approaches. Methods may leverage a user'sprofile, user and conversation context and knowledge, and the like, ofboth conversation parties to automatically identify (e.g., usingtechniques such as machine or deep learning) types of language, and thenemploy rule or statistical techniques to generate the appropriate thecontext.

Transformations may include rewriting messages or documents using style,tone, argumentation, coherence and structure, and the like, that theuser would understand and react positively to, such as depending on thecontext, goals, and recipient background. Methods may include machineand deep learning techniques which can leverage parallel corpora of longsentences or passages with their summarized rewrites, as well asleveraging vast amounts of unlabeled data.

Transformations may include masking or rephrasing the language that theuser would not like to see (e.g., offensive language, negative triggers,cliches, and the like) of certain words based on user's statedpreferences or previously observed reactions. Such transformations maytake the form of classification machine or deep learning algorithms tostrike out words or phrases or sequence to sequence algorithms whichautomatically convert one text to another, such as neural machinetranslation or paraphrase. Data employed ranges from manually orautomatically curated lists of commonly overused phrases may be employed(either generally, by domain, by communication party, and the like) toparallel data sets which consists of phrases, sentences, or documentswith the target language and their corrections.

Transformations may include adding missing context, such as explainingterms, abbreviations, slang, idioms, and the like, that can beunfamiliar to the user. Methods may include using manually andautomatically curated dictionaries and reference sources as part of ruleor statistical approaches. Methods may leverage the user's profile, userand conversation context and knowledge of both conversation parties toautomatically identify (e.g., using techniques such as machine or deeplearning) parts of communication which need more context, and thenemploy rule or statistical techniques to generate the appropriate thecontext.

Transforming incoming communications may methods include, but are notlimited to, rule-based, machine learning, deep learning or otherstatistical supervised, semi-supervised and unsupervised techniques.Readers may choose message or document types that they would like to beautomatically modified. Alternatively, users may request the system tomodify specific messages or documents on demand, such as by applyingspecific filters or transformations. Users may be able to view theoriginal version even if the message/document was automaticallymodified.

In embodiments, and with reference to FIG. 11, the AIA may provide forfeedback with regard to communication interactions, such as for audioand/or video communications 1102 being captured and processed 1104:identifying participants, verbal and non-verbal content and context1106; maintaining a digital representation of the conversation 1108,continuously updating a representation of the conversation thread 1110;analyzing incoming and outgoing messages and providing real-timeassistance 1112, providing feedback 1114, and the like.

In embodiments, the user may be able to specify whether they want totransform incoming communications automatically or manually, showing theoriginal before or after transformation, and the like. In a non-limitingexample, and referring to FIG. 12, based on receiver preferences 1202,at first process step 1204, the user interface may intercept an incomingcommunication to a receiver and classify the communication, such as by atype of communication, who the user is, and the like. In a secondprocess step 1206, the user interface may decide whether to transformthe communication, such as automatically based on user preferences ordecided by the receiver based on an interaction between the receiver andthe user interface. In a third process step 1208, the user interfacedetermines that the incoming communication should not be immediatelytransformed, showing the receiver the original communication andoffering the ability to transform. In a forth process step 1210, theuser interface determines that the incoming communication should beimmediately transformed before displaying the communication to thereceiver and offering to also show the receiver the originalcommunication. This is meant to be merely a non-limiting example of howthe AIA may be configured to transform an incoming communication,illustrating some basic features that may be provided to the user.Transformation types, such as described herein, may also be selectableby the user through a user interface, such as improvements inreadability, creating greater conciseness, providing summarization,formatting the communication, providing language cleanup, injectingcontext into the communication, and the like.

Augmenting incoming communications to a user may be provided through auser interface, such as providing the ability for the user to modify thecommunication after it's sent, ability for the receiver to interact withand provide feedback on or react to any part of the communication,ability for the receiver to transform incoming communications,collecting user preferences in the context of incoming communication,such as through iterative questions and tracking reactions, and thelike. The user interface may send the content of the communications as alink reference to an object stored on a server (e.g., in the cloud) thatrenders on the client side in an interactive form allowing: 1) a userreceiving a communication to interact with and provide feedback on thecommunication or any part of the communication and 2) a user sending acommunication to modify the communication at any point after it was sent(e.g., if the user thinks of something else, finds a mistake, calms downand wishes they wouldn't have set the communication, and the like). Thiswould enable users to control their content over time, even when sentthrough third-party applications (e.g., email applications), where userscould change and/or delete their content at any point in the future andprovide a receiver with an option to modify communications they receive,such as based on their preferences.

In embodiments, a sender portion of the user interface may providespecific feedback to help users compose communications and to increaseeffectiveness by targeting communications to the receiver specificprofiles and preferences, such as with respect to vocabulary, style,context, accuracy, the best time to send the communication, and thelike.

In embodiments, a receiver portion of the user interface may displaycommunications in an interactive format (e.g., embedded or a pop-up webpage object) allowing receivers to interact with and provide feedback onthe communication even if they are not using the AIA client application,which may also provide users a chance to modify the communication afterit's sent. For instance, receivers may react to incoming communicationsby highlighting any fragment and selecting an emoji or writing a commentto it that would be immediately available to the user (e.g., the userwould get notified and be able to see the emoji and/or comment byopening the communication). If nothing is highlighted, the reaction maybe applied to the entire communication. If the communication was sent tomultiple people or shared with a group, the receiver may choose to sharethe reaction only with the user, with all receivers, (e.g., everyone onthe thread), with a subset of receivers, or keep it private but visibleto MA. AIA may process the reactions and use them to improve the rulesand models optimizing incoming communication for the receiver andassisting the user(s) in communication with the receiver.

The user interface may enable the receiver to transform incomingmessages, providing reader (receiver) with the ability to modifycommunications they receive, such as with the transformations describedherein. Each transformation type, such as described herein, may beprovided with a separate filter that can be turned on or off.Additionally, the receiver may specify the types of incomingcommunication they would like the filters to be applied toautomatically, while other communications are modified on demand byapplying specific filters. Alternatively, users may request the systemto modify specific incoming communications (e.g., messages, documents,and the like, as described herein) on demand, such as by applyingspecific filters and/or transformations. Users may be able to view theoriginal version of an incoming message even if the communication wasautomatically modified. In the process, user preferences may becollected in the context of incoming communications through iterativequestions and tracking of reactions.

In embodiments, the system may send a message (e.g., content) over anetwork, sending a link to the message that is stored on AIA servers orcloud. If the receivers have the AIA installed (e.g., as a browser orcommunication application extension), they will see the actual messageinstead of the link (e.g., AIA will display the actual message insteadof the link). The receivers that don't have the AIA installed, may onlysee the link but will be able to follow this link to view the messageand interact with it (e.g., in the browser). The sender may modify themessage at any time, and all the receivers may be enabled to immediatelysee the updated version. If some receiver has an optimized version ofthe message open (e.g., a feature of the AIA augmenting incomingcommunication), this optimized version may automatically re-generatewhen the sender modifies the original message. Receivers may interactwith the message and provide instant feedback (e.g., by selecting aportion of the message and reacting with an emoticon or a comment).Receivers may control who sees the feedback, such as only the sender,everybody on the thread, or just the receiver and AIA (e.g., so that AIAcould better optimize future messages for the receiver). Additionally,the users may have all the AIA features for optimizing outgoingcommunication and augmenting incoming communication.

In embodiments, the user interface may deliver iterative questions to auser with respect to the receiver's communication preferences and/orfeedback on specific communications to hone the AIA's understanding ofeach receiver's preferences. These preferences may be used by the systemto modify incoming communications to the receiver and/or to help thesending user(s) optimize their communication with the receiver.Receivers may be motivated to provide feedback so that the AIA couldbetter transform communications to their liking. Receivers may choose toshare their feedback on incoming communications with users to providereceivers with improved versions of the modified communications of somecommunications they receive based on the shared feedback and preferencesprovided.

In transforming an incoming communication, the AIA may recordpreferences, it may intercept incoming communication, classify it, anddecide whether it should be transformed before being displayed to thereader and what transformations should be applied, as based on thereceiver's preferences. Then filters can then be run based onpreferences (e.g., may be a combination of rule-based andmachine-learning-based filter implementations). The AIA can then presentthe transformed version to the reader based on the preferences. Inembodiments, user preferences may enable the selection of otherfunctions, such as the receiver being able to switch between thetransformed and the original versions (e.g., by clicking a link or tab),be able to highlight fragments and react with an emoji or provide acomment, request to modify a specific communication that AIA did notautomatically modify, and the like. In embodiments, AIA may learn tomodify communications of a certain type based on what the receiverrequests to modify most frequently. In that case, AIA may ask whetherthe receiver wants to always (or never) modify similar communications,or communication from certain users.

In embodiments, the user interface and user experience may beimplemented through integration with third-party platforms andcommunication channels (e.g., as an extension or a plug-in), as afeature of a communication platform or client (e.g., built into amessenger, email app, and the like), through a mobile platform (e.g.,communication may be composed in a dedicated mobile app), stored in thecloud, and the like. Implementation may utilize a link sent through acommunication channel (e.g., text message), where when the receiverclicks the link, it may launch a dedicated app or open the communicationin a web browser. If the receiver is not on the platform or doesn't havethe extension/app, they could get a link that would open thecommunication in a web browser, such as with interactive features.

If the sender modifies a communication that has already been sent, itmay be updated on all clients that have it open. Synchronization betweenthe server-side copy and the ones displayed to the sender and thereceivers may be implemented using technology such as operationaltransformation, differential synchronization or conflict-free replicateddata types. Transformed receiver-side versions may also be re-generated.

In embodiments, the user interface may capture receiver preferences,such as for contributing to their profiles, through asking a set ofquestions/presenting settings during user onboarding (e.g., how theywould like the texts to be modified, what they would not want to see,which kind of communication they would like to always or never modify,and the like), through prompting the user to provide more answers andsettings later, through tracking their communication and reactions andsuggesting settings that match their communication style/behavior andreactions, and the like.

The user interface may also provide for tracking communication andcontent consumption habits and preferences for a receiver, providinginsights and recommendations for better productivity and communicationimpact.

In embodiments, the AIA may augment outgoing voice and video messages.For instance, the AIA may intercept a voice or a video message, check itfor correctness, clarity, and effectiveness in the background, and alertthe user in case any significant issues are found, and suggestimprovements (e.g., via text or voice output). In the process, thesystem may ask the user for additional details to get more context andidentify a receiver if necessary for targeted or more effectivesuggestions. The AIA may augment conversations, such as to help the userunderstand the reactions of the receiver(s), interpret hidden andnon-verbal signals, suggest what to say next to mitigate or preventpossible tension or misunderstanding, suggest adjusting vocabulary ortalking speed based on the receiver's language proficiency, and thelike. The AIA may augment incoming communication, such as transformingincoming communications or other content the receiver consumes into amore digestible form. For instance, the AIA may summarize longer textsand messages so that processing them takes less time, rewrite texts andmessages using vocabulary and readability level that the receiver wouldunderstand (e.g., depending on his or her language proficiency level andbackground), rewrite texts and messages with respect to communicationstructure (e.g., prose vs. bullet listings), and the like.

In embodiments, the AIA may provide the ability to augment real-timeconversations being carried out through augmented reality (AR) orvirtual reality (VR) platforms through an AIA AR/VR communicationfacility, which provides communication assistance (augmentedcommunication) to users (senders as well as receivers). The AR/VRcommunication facility may be implemented through an application orapplication extension for a third-party AR/VR platform or a feature ofan AR/VR platform, and support both asynchronous communication(improving communications that are outgoing, recorded, dictated, and thelike) and real-time conversations. Although the AR/VR communicationfacility is described primarily with respect to AR and VR technologyplatforms, and the sender's point of view, the AR/VR communicationfacility may work with any wearable device including a microphone tocapture dictated or recorded communication(s), a privacy output displaycapability to provide assistance and suggestions to the user (e.g., anAR or VR display, smart eyeglasses, heads-up display, smart contactlenses, head-worn virtual retina display, and the like), and an in-earspeaker. Optionally, other functional resources may be utilized by theAR/VR communication facility, such as a camera to read body signals in aconversation, identify or recognize specific people in a conversation,recognize speech (from mouth movements), recognize eye movements,recognize written text, intercept video communications, and the like.Body sensors (implantable or not) may be used to capture emotions, levelof stress, and the like, that may be associated with communication. Abrain-computer interface may be used to capture reactions, intentions,thoughts, and the like. In embodiments, the AR/VR communication facilitymay be implemented with a smart speaker to intercept voicecommunications, analyze them, and either translate them intobetter-formed text and then read back or display them in a targetapplication for confirmation or iterations. The AR/VR communicationfacility may provide feedback/suggestions via voice or a companionapplication display (e.g., smartphone, smart-glasses, and the like)before finalizing and sending a recorded communication. The AR/VRcommunication facility may be implemented with a VR platform to augmentconversations between people as avatars in virtual reality throughcapturing and improving recorded communication and assisting inreal-time conversations. In embodiments, observed communication can betracked and analyzed to generate insights and provide feedback to theuser over time, such as processed on the device, processed offline,processed in a hybrid client-server system (e.g., partially processed onthe device), and the like, such as with offline or embedded models.

In embodiments, the AR/VR communication facility may augment outgoingrecorded or asynchronous voice or video communication. For instance, theAR/VR communication facility may intercept a voice or a videocommunication the user has recorded through a smart speaker or a smartdevice camera (e.g., the ability to connect to a network via differentwireless protocols can operate to some extent interactively andautonomously, such as by interacting with its user through processingvoice commands/input and exchanging the data with other smart devicesremotely), extract the content (e.g., text and non-verbal signals fromthe voice tone, pauses, body language, and the like), analyzecorrectness, clarity, and effectiveness, alert the user in case of anysignificant issues, and suggest improvements. In the process, the AR/VRcommunication facility may ask the user for additional details to getmore context and identify a receiver if necessary for targeted or moreeffective suggestions. For instance, a user may send an audio or videocommunication through an appropriately equipped headset. The AR/VRcommunication facility may intercept the audio or video (e.g., throughan integration with a message recording application, as a feature of acommunication platform that supports devices with built-in cameras ormicrophones, and the like). The AR/VR communication facility may thenprocess the captured audio or video and extract verbal content. Withaudio, the verbal content may utilize audio speech-to-text conversion,and with video, the extraction may utilize video speech-to-textconversion (e.g., possibly in combination with audio speechrecognition). The system may then extract non-verbal signals, such aswith audio, identifying psycho-emotional state (e.g., emotions, mood,stress level, and the like) and implicit intents of the user based onthe tone and pitch of the voice, speech patterns (e.g., pauses,interjections and other sounds, and the like, or with video, identifyingpsycho-emotional state (e.g., emotions, mood, stress level, and thelike) and implicit intents of the user (e.g., based on facialexpressions, posture, gestures, and the like) for identifyingculture-specific body language. This may provide an accounting forpossible multiple meanings and misinterpretations of non-verbal signalsby the receivers. The AR/VR communication facility may then capture thecontext of the communication (e.g., level of background noise, visualbackground, time of day and the like); identify the receiver, such as byobtaining an identifier from the communication platform (or by askingthe user); and analyze verbal and non-verbal content for correctness,clarity, and effectiveness, given the receiver and the context of thecommunication. The AR/VR communication facility may then provide generalfeedback on the communication, such as suggesting modifications orrewrites to the communication (or parts of the communication). In thecase of an asynchronous communication (e.g., not yet interactive withthe receiver, the user has more time and available focus, so the AR/VRcommunication facility may be able to rewrite longer phrases or theentire communication and present them to the user for review with aslight delay without significant disruption to the communication flow.At this point the user may decide to re-record the communication ordiscard the feedback/suggestions. For instance, if the user discards thefeedback (such as repeatedly), the AR/VR communication facility may askwhether the user would like to mute AIA for this type of communicationor this receiver and update the preferences accordingly. If the userre-records the communication, the AR/VR communication facility mayintercept the new recording, re-analyze, compare to the previouscommunication, and provide feedback on the new version (e.g., providingencouragement in case of positive changes).

In embodiments, the AR/VR communication facility may augment real-timeconversations, such as to help users understand each the reactions,interpret non-verbal signals, suggest what to say next to mitigate orprevent possible misunderstanding or tension, suggest adjustingvocabulary, articulation, or talking speed based on a receiver'slanguage proficiency, and the like. For instance, for the AR/VRcommunication facility operating in conjunction with a wearable ARdevice with built-in microphone and camera, the AR/VR communicationfacility may capture audio and video communication data using thewearable device. The AR/VR communication facility may process the audioand video, separating audio and video signals from individualcommunication parties (e.g., recognizing faces and bodies in the video,extracting voices from the audio signal), matching audio and video fromeach communication party (e.g., tracking sound direction and/or lipmoves to match individuals' video and audio streams), and filteringvoices and video of individuals and objects that don't participate inthe conversation. The AR/VR communication facility may apply face andbody tracking to the video of communication parties, such as to be ableto recognize facial expressions, posture, and gestures, identify thevoice of the user, identify other communication parties (e.g., one ormore individuals the user is communicating with) using facial or voicerecognition or data/identifiers sent by their devices. The AR/VRcommunication facility may extract verbal content from the capturedvoice or lip/mouth movements (e.g., using audio speech to text and/orvideo speech to text conversion), extract non-verbal signals from thecaptured voice and face/body tracking (e.g., identifyingpsycho-emotional state of the parties, such as emotions, mood, stresslevel, and the like) based on the tone/pitch of the voice, speechpatterns (e.g., pauses, interjections and other sounds, and the like)based on facial expressions, posture, and gestures, identifyculture-specific body language, identify implicit intents of the partiesbased on their tone, face and body language, and the like. The AR/VRcommunication facility may recognize other context, such as currentcharacteristics and changes in the environment (e.g., background noise,relative spatial positions of communication parties, location,geography, and the like), identify the characteristics of the users fromcommunication profiles or audio/video (e.g., native/non-native, based onthe accent), and process body sensor data.

In another instance, the AR/VR communication facility may operate inconjunction with a wearable VR device, where in this example it isassumed that the conversation happens between avatars capable of speechand body language mimicking human users, controlled by human users, orgenerated by AI agents. In this instance, the process would begin withcapturing data from a VR platform. This may be raw audio and video as inthe case of AR, separate speech and visual signals from eachparticipant, parsed data (e.g., textual messages, emotion/intentsignals, and the like), and/or meta-data (e.g., as identifiers andcharacteristics of the avatars or the parties represented as avatars).The AR/VR communication facility may extract identities andcharacteristics of the parties, verbal content, non-verbal signals, andconversation context from available data. The AR/VR communicationfacility may maintain a digital representation of the conversation,splitting the verbal content from each communication party intoindividual communications, maintaining a digital representation of theconversation thread (e.g., updating it in real time) by tracking thesequence and relations between the communications (e.g., who eachcommunication originates from, is addressed to, and which previouscommunications it references/answers or expands on), and associatingcaptured non-verbal signals with individual communications (e.g.,tagging communications with non-verbal clues). The AR/VR communicationfacility may analyze incoming and outgoing communications and providereal-time assistance. For instance, the AR/VR communication facility maygive the user an option to view text transcriptions of others'communications as subtitles via an VR display, such as smart eyeglasses,HUD, contact lenses, virtual retina display, or the like. This may helpthe user understand the communications in cases of unfamiliar accents,limited listening proficiency (e.g., a user listening in a language thatis not a user's native language), or inability to hear well (e.g., dueto background noise, hearing impairment, and the like). The AR/VRcommunication facility may also translate the speech from one or morelanguages into a language the user can understand and display thetranslations in a similar way. The AR/VR communication facility mayaugment what the user sees and/or hears with a representation ofnon-verbal signals from other communication parties (and possibly theuser themselves for a self-check). The AR/VR communication facility mayoverlay the image of communication parties on a VR display with a label,an emoticon, or an ideogram indicating their psycho-emotional state(e.g., emotion, mood, stress level, and the like), explaining a recentgesture (e.g., a sign of a welcome, gratitude, and the like, such as inthe party's culture) or possible implicit intent (e.g., a desire forintimacy, a lack of interest, increase/decrease in domination, and thelike), such as based on different kinds of recognized body language.

The AR/VR communication facility may warn the user when there is anincreased risk of miscommunication, conflict, or ineffectiveness in theconversation, such as when the user talks too fast to someone who is anon-native speaker, the AR/VR communication facility may suggestlowering the pace or increasing articulation, or when the user uses anidiom that the other party in the conversation may not understand, theAR/VR communication facility may suggest a simpler phrasing or justdisplay the idiom in question and recommend rephrasing it. The AR/VRcommunication facility may analyze the user's pronunciation and issue awarning in case of incorrectly pronounced words that may lead to amisunderstanding, and suggest the correct pronunciation (e.g., withgenerated voice or text notation); identify a grammatical error in theuser's speech and issue a warning when it may lead to miscommunicationor bad perception, and display a corrected version of the phrase so thatthe user could repeat and correct themselves; detect inappropriatevocabulary or style choices in the user's speech and issue a warningwhen the other party may not perceive them well (e.g., based on theircommunication profile), and suggest dynamically adjusting the style orlanguage and/or call out words and phrases to avoid; detect inflammatorylanguage or non-constructive non-verbal signals in the user's speech orbody language (e.g., voice tone, proximity change, and the like), andalert the user and suggest taking a pause; detect negative signals fromthe other party (e.g., mood change, increased stress level, loss ofattention/interest, and the like) and alert the user to be aware orattentive, and consider changing the course of the conversation; suggestphrases that would be appropriate at a particular point in theconversation, such as saying thank you; detect unanswered questions fromthe previous communication(s) and prompt the user to address them;detect when the user (e.g., repeatedly) interrupts the other party anddisplays a warning when that happens; and the like. The warnings may bea visual or an audio alert with a short communication from AIA. The usermay be able to mute/unmute the alerts and adjust the sensitivity. AIAmay also adjust sensitivity automatically based on the communicationparties involved, the relationship between them, and the type/tone ofthe conversation (e.g., it may mute all but most critical alerts duringa casual chat between good friends and increase the threshold during abusiness meeting with unfamiliar participants). In embodiments, theAR/VR communication facility may provide feedback and analytics afterthe conversation, analytics/statistics to the user based on the recentconversation(s), actionable feedback to the user based on how the recentconversation(s) went, and the like.

The AR/VR communication facility may detect a user's emotional orphysiological state to correlate with their current communication styleand reactions to previous communications, such as through biometric datafrom the user (e.g., from a wearable device), voice indicators (e.g.,tone captured with a built-in microphone), visual indicators (e.g., fromfacial expressions or body language captured with a built-in camera),and the like. For instance, a user may be wearing a wearable device ormultiple devices that monitor temperature, heart rate, galvanic skinresponse, and blood sugar; voice signals (e.g., vocal inflections,tone/pitch, volume, speech patterns, and the like), facial expressions,and body language. Measurements and changes in this data may be comparedto an emotional profile, such as stored in the user's communicationprofile, and measurements in a population. The AR/VR communicationfacility may then be able to determine if these indicators are adverselyaffecting the user's communications, such as when a spike in bodytemperature, heart rate, and low blood sugar are detected, making aperson more likely to communicate in a frustrated or angry manner. TheAR/VR communication facility may then prompt the user to be aware ofsuch potential influences, and offer communication adjustmentsuggestions (e.g., taking a pause, a breath, changing the tone/language,and the like). The AR/VR communication facility may also correlatedetected emotions with the content and/or context of the user and usethis as training data for modules that predict reactions and also forupdating the user's communication profile.

In an example embodiment, and referring to FIG. 13, the AR/VRcommunication facility may intercept an audio and/or video communication1302 and extract verbal and non-verbal content 1304, which may beanalyzed for effectiveness of verbal and non-verbal content 1308, giventhe context and the recipient, and capturing the content identifying therecipient 1306. For repeated takes on the message, the system maycompare the analysis of the previous take and providing incrementalfeedback 1310, providing general feedback 1312, and suggesting specificmodifications to the message 1314. The user may then decide 1316 whetherto take on another message or not, and if yes, run through the processagain, and if no exit the routine 1318

FIGS. 14-20 illustrate a non-limiting example of an interaction with auser who is generating a textual message targeted to a receiver ‘JohnDoe’, where the AIA is assisting the user based on the receiver'scommunication profile. In embodiments, the AIA may also be assisting theuser through access to the user and/or receiver's communication profile(e.g., how does the user know John Doe), contextual informationavailable from past interactions between the user and John Doe (e.g.,how well have interactions gone before), current events (e.g., making apassing comment about an important item in the news where the AIA hasdetermined a level of relevancy between the event and the message), andthe like. At a first computer user interface screen view 1400, the useris able to view aspects of John Doe's communication profile, such asincluding the attributes of being detail-oriented, methodical, decisive,appreciates formality, has strong social skills, and the like. Thisenables the user to orient themselves before beginning the process ofwriting the textual message to John.

At a second computer user interface screen view 15000, the user beginsto draft the message. At a third computer user interface screen view1600, the user is well along in the drafting of the message, perhapseven at the end of a first draft. The AIA may automatically evaluate thetext, such as continuously as the user types, at periodic points duringdrafting, when the user pauses, and the like. The user may also have theoption of requesting the AIA to evaluate the text at any point in time.In this instance, the AIA provides feedback to the user that indicates“this message may not appeal to John”, and offers some indications as towhy, including a note that “your message lacks a thesis.” The AIAadditionally prompts the user to improve the message by asking “what isthe goal of this message?”, with options, including “make a decision”and “give an update”. This feedback is meant to be illustrative, and notlimiting in any way, but used as one example of how the AIA mightattempt to aid the user in generating a more effective message. Inembodiments, the “make a decision” and “give an update” may be buttonsto be selected by the user, which would lead the AIA to provide furtherrefined suggestions, such as specific suggestions as to how the messagecould be improved with a goal in mind. Although this illustrativeexample shows visual indications and suggestions, feedback to the usercould be audible, or be provided as a combination of visual and audible.For instance, the AIA could audibly ask the user about the goal of themessage and offer suggestions. In turn, the user could audibly select achoice, such as saying “make a decision.”

In a forth computer user interface screen view 1700, the user may viewand approve a change in the message due to interaction with the AIA,continue typing, receive additional indications of text content thatcould potentially improve the effectiveness of the message, and thelike. In a fifth computer user interface screen view 1800, the user mayreceive an additional suggestion from the MA, where in this case the AIAsuggests adding a “personal touch” and for the user to “consider usingthe following phrases”, including “low hanging fruit”, “it's up to you”,“use your best judgement”, “kill two birds with one stone”, and thelike. Alternately, the AIA may make suggestions that target thereceiver's language, geographic location, culture, and the like, such assuggesting idioms that the receiver is familiar with based on priorusage, geographic indicators, and the like. In embodiments, the user mayaccept one of the suggestions by having the AIA insert a selected phraseinto an appropriate place in the message, choose from a number ofoptions as to where the selected phrase could be inserted, manuallyinsert the message, and the like, where the interchange between the userand the AIA may be through visual and/or audible communication. In asixth computer user interface screen view 1900, the AIA provides anotherfeedback to the draft message that includes “support your message withfacts”, and to “add facts to strengthen your message”, such as withdata, feedback, links to sources, and the like. In a seventh computeruser interface screen view 2000, the user, having improved the messagethrough interaction with the AIA, emails the message to John Doe.Through the process of feedback and interactive suggestions, the AIA hasthus enabled the user to improve the message.

In embodiments, the AIA may provide feedback and suggestions to the userin an automatic interactive step-wise process such as illustratedthrough FIGS. 21-27. Alternately, the user may elect to have the processprovided in a more immediate manner, such as where the AIA analyzes themessage and provides options all at once for the user to select.Further, the user may be limited by time, and thus enable the AIA tosuggest an improved message without options, such as where the MAutilizes previous interactions with the user to determine what the useris likely to select, or utilizing previous interactions with the user toselect a different set of options than has been previously selected(e.g., to keep the user's messages diverse), and the like.

FIGS. 21-27 illustrate a non-limiting example of an interaction of auser in an exchange with a second user (e.g., face-to-face with messagecontent and AIA feedback displayed to the user, texting with a remotereceiver showing the text dialog along with MA feedback). Although thedepiction is of a textual exchange as viewed on glasses (e.g., augmentedreality glasses, or any glasses enabled content display facility), theexchange is more generally representative of an exchange that could beimplemented an any of a variety of computer platforms, such as on asmart phone, a smart watch, a tablet, a laptop computer, and the like.Further, although the exchange is depicted as a visual presentation ofthe exchange, the exchange could also have been at least in part verbal,such as where the conversation between the users is verbal (e.g., aphone call over a cellular network), but where the AIA feedback ispresented in a visual text format for the user to read during the verbalexchange. Further, the entire exchange, including the exchange betweenusers, and the feedback presented from the AIA, are both verbal, such asthe MA providing verbal feedback to the user as the conversation betweenusers takes place.

In a first computer user interface screen view 2100, the user initiatesa conversation with another user, saying “Alex: How do we improve ourplanning process this time”. In a second computer user interface screenview 2200, the user transmits a portion of conversation, “we need to seethe forest for the trees. That will allow us to create a better plan.”In a third computer user interface screen view 2300, the MA providesfeedback to the user, “‘Forest for the trees” is an idiom that Alexmight not understand, as he is not a native speaker. In this instance,the AIA, having access to the receiver's communication profile, remindsthe user that the receiver is a native speaker, and as such shouldmodify the conversation appropriately. As a result, as shown in a forthcomputer user interface screen view 2400, the user follows up with aclarifying statement, “Let me clarify what I meant by ‘Forest for thetrees’. It means to see the big picture and not focus too much on thedetails.” In a fifth computer user interface screen view 2500, the usertells the receiver (by voice) “Hey Alex, I'm heading out to lunch, wouldyou like to join?” In a sixth computer user interface screen view 2600,the AIA presents feedback to the user to “Speak slower please” to alertthe user to the mismatch between the speed of the user's speech and theability of the receiver to comprehend. In this instance, the AIA mayhave been monitoring the speed of the user's speech because the AIA hadaccess to the receiver's communication profile that identified thereceiver was a non-native speaker, and where the AIA then flagged speedof user speech for monitoring. Alternatively, the AIA may havedetermined the receiver to be a non-native speaker by monitoring thespeech of the receiver. As a result of the AIA monitoring the user'sspeed of speech, in a seventh computer user interface screen view 2700,the user restates the passage more slowly as “If you could join forlunch, we could go to Stone or Osha. What do you think?” Throughout theconversation, the AIA, through interactive feedback, enabled the user tomake real-time adjustments to the conversation to accommodate thereceiver being a non-native speaker, such as where the non-nativespeaker may be unfamiliar with idioms of the non-native language,limited to a speed of conversation in their non-native language, and thelike.

In embodiments, the AIA may suggest a message template and/or modalitybased on a given goal and context. For instance, the AIA may compose amessage asking whether ‘x’ will show up for dinner and deliver themessage through an appropriate communication channel (e.g., email, textmessage, and the like). In embodiments, a communication channel may beselected based on the communication profile of the receiver, such asincluding data pertaining to preferences and behavior. For instance, thesystem may select a communication channel that the receiver uses mostoften or is most likely to respond to, such as based on a statisticalanalysis of observed behavior. In embodiments, a message may be modifiedbased on feedback from the AIA to the user, and then transformed oradjusted in format as necessary to accommodate the selectedcommunication channel. Multiple formatted versions of the message may becreated for multiple communication channels, such as when a user wantsto broadcast a message to multiple receivers on multiple communicationchannels.

In embodiments, the AIA may apply a style ‘filter’ based on a desiredimpact and audience. For example, a humor filter may be applied whencomposing a text on a smart phone to make a friend laugh, or a formalityfilter may be applied to improve the effectiveness of an email applyingfor a job. The AIA may also adjust the style or tone of a message, suchas adjusting the tone of an emotionally charged message, so it is lesslikely to offend the receiver. For example, the AIA may help the userrewrite a message using a non-violent communication framework toalleviate conflict. The AIA may also suggest a fact or a clarifyingpoint to a message based on world knowledge (e.g., current news,historical context, and the like), shared context of the sender-receiverpair (e.g., conversation history), and the like. The AIA may render themessage differently and/or deliver it through different filters, styles,clarifying points, and communication channels, to different receivers(e.g., sending a dinner invitation to N people in a personalized form,or rendering several versions of a news article for a number ofdifferent audiences), depending upon the goals associated with thedifferent receivers.

In embodiments, the AIA may provide multiple modes and levels offeedback, such as through making predictions relative to receiverreaction, message impact, and success, given the content (e.g., messagedraft), targets (e.g., goals/desired impact, audience/receiver,style/format restrictions, and the like), and context (user'scommunication profiles, relationship, current states/emotions, priorcommunication history, the environment, world knowledge, and the like)of the message. In embodiments, the AIA may provide for a default set ofmessaging goals with regard to providing feedback, such as that amessage should be accurate (e.g., error-free), not ambiguous (e.g.,clear), not harmful or offensive, doesn't cause or escalate conflict,minimizes the risk of misunderstanding, and the like. Explicit goals,such as to inform, persuade, entertain, and the like, may be selected bythe user.

In embodiments, the AIA may be able to identify parts of a message thatcan be improved, generate alternatives, rank them, and suggest thosethat maximize achieving the desired outcome. For instance, the messagemay be determined to be too informal, and the AIA may identify plausiblecorrections with varying levels of formality, rank them all based on howformal they are, and present the rankings to the user for selection. Inembodiments, the AIA may provide personal communication analytics andcoaching, such as including quantification and tracking of informationconsumption, communication patterns, andhealth-productivity-relationship effects, and provide advice a as aglobal means of providing feedback to the user.

In embodiments, the AIA may be implemented through a number of naturallanguage processing and machine learning methods, such as support vectormachines (SVM), random forest, boosting machines, ensemble methods,matrix factorization, deep learning (e.g., seq2seq models, bi-LSTMS,CNNs, RNNs, and the like), generative adversarial networks, pointernetworks, unsupervised learning approaches, reinforcement learning, andthe like. The AIA may use collected, acquired, or artificially createdtraining data for training the corresponding learning algorithms. Incertain embodiments, the AIA may collect, generate or annotate trainingdata for training the corresponding learning algorithms. These methodswill cover aspects of the AIA such as a general model of languageeffectiveness, genre-domain model, group model (e.g., groups ofindividuals), individual model (e.g., a single targeted individual),pipelined-ensemble model structure, predictive impact modeling, languagecorrectness model (e.g., to check and correct rewrites), meaningconsistency model (e.g., to ensure re-writes do not alter meaning),individual-authentic style models (e.g., to personalize generatedlanguage), world knowledge model, and the like. The AIA may utilizemulti-layer neural networks (e.g., for transferring a writing style frominformal to formal using neural techniques). In embodiments, models maybe updated or retrained automatically or manually, where training datamay utilize a corpus of original messages tagged with contextualinformation as meta-data (e.g., domain and message type, message goals,user identifier, target audience or receiver identifier, modality,communication channel, triggered outcomes, and the like, such as actionsand emotions), a corpus of conversations and related reactions ofparticipants, a parallel corpus of original and revised/enhancedmessages, communication history of users, feedback from users, such asthe applied and rejected suggestions, survey results, and the like.

In embodiments, the AIA may provide for automatic writing correction,which may be utilized by the AIA in providing improved communicationsbetween individuals such as described herein. For instance, inembodiments the AIA may optionally utilize a cloud-sourced plurality ofhuman editors in a managed human augmented automatic system, where theAIA may automatically send at least a portion of a message to thecloud-sourced plurality of human editors, such as when the MA encountersa writing element that exceeds a perception threshold during automaticprocessing (e.g., a threshold over which the MA cannot acceptablyunderstand the meaning of a passage). The AIA may manage thecloud-sourced plurality of human editors, or interface with a managementsystem that manages the cloud-sourced plurality of human editors. Forexample, the AIA may evaluate a user text and encounter three portionsof the user text that may benefit from enhancement with respect to thecapabilities of the AIA, as described herein. However, the first portionis evaluated and determined to exceed a perception threshold, at whichpoint the AIA distributes the first portion amongst the cloud-sourcedplurality of human editors for determination of a writing enhancement.Once the AIA receives a response, the AIA may then incorporate theresponse, together with automatic modifications due to the second andthird portions, into a feedback to the user.

The AIA may provide a service to users where a combination of automaticand human interaction improves the user's communications as describedherein. This human augmented AIA service may utilize a cloud-sourcedplurality of human editors, a team of dedicated writing specialistsfamiliar with the user's writing style and target audience, and thelike, to provide the user with rapid response communication refinementthat leverages the automatic capabilities of the AIA and the deepexperience of a human writing expert. The human augmented AIA servicemay provide a range of combined automatic-human augmented writingenhancement, such as ranging from an augmented enhancement that reliesentirely on the automated capabilities of the AIA as described herein,to a blended automation and human augmentation, to the use of a humanwriting expert reacting in real-time without utilizing the automaticcapabilities of the AIA. In embodiments, the AIA may manage the use ofhuman writing experts based on user and/or receiver preferences and pastbehavior. For instance, when a user is composing a written communicationthey may indicate a preference for an extent of human personalizationvs. automation, such as depending on the topic and goal of thecommunication, based on lead time, based on personal preferences, andthe like. In embodiments, the AIA may determine this through the user'scommunication profile and behavior, or as directed by the user.

In an example use-case of the human augmented AIA service, suppose theuser begins composing an email to a customer and is using the AIA towork through refinements, as described herein. But then the user isinterrupted and doesn't return to the drafting until the end of thebusiness day. When the user returns to the email drafting the AIAdetects that the user is behaving in a manner that indicates that theemail drafting is now more urgent (e.g., through a voice tone, use oflanguage, change in editing technique, and the like), and the AIAmoderates the process by incorporating a human writing expert into theprocess. For instance, a human writing expert could act as a monitor tothe automated AIA interchange with the user and interject as needed, thehuman editor could interject themselves more fully into the interchangesuch as interacting directly with the user but utilizing the automatedAIA capabilities as a tool, the AIA and/or human editor could sense ahigh-level of urgency from the user and have the human writing experttake over the recommendations process in order to expedite the process,and the like.

In another example use-case of the human augmented AIA service, supposethe user wants to utilize all the capabilities of both the automated AIA(as described herein) and the deep experience of a team of human writingexperts (e.g., that are assigned to work with the user). In thisinstance, the user benefits from all the historical, global, andpredictive capacity of the AIA, plus the interaction with a humanwriting expert. Based on user preferences, a custom human augmented AIAservice may be developed and maintained, such as to the extent the userwants the automatic AIA system or human editor to dominate in theinteraction. For instance, the user may prefer the automatic AIA processas the default, where the AIA system only accesses a human writingexpert when a perception threshold is exceeded (e.g., a level ofunderstanding for a particular passage or writing issue, a level ofconfidence in a refinement choice), or the user may prefer a humaneditor lead the process. In embodiments, when a human editor is involvedin a message refinement process, the human editor may utilize the AIA asa tool, acting as ‘a user’ as described herein. That is, a user mayprefer a human writing expert to interfacing with, but where the humanwriting expert utilizes the AIA as described herein. In thisuser-editor-AIA configuration, the human writing expert may act as auser-proxy between the user and the AIA.

The AIA, through access to user communication profiles, goals, context,world knowledge, modality, and the like, may be able to affectmodifications to a user's communications, either directly or indirectly,to increase the user's effectiveness in communications with a receiver,and thus provide a valuable facility to the user in communicationsamongst a diverse population of receivers and audiences.

The programmed methods and/or instructions described herein may bedeployed in part or in whole through a machine that executes computersoftware, program codes, and/or instructions on a processor orprocessors. “Processor” used herein is synonymous with the plural“processors” and the two terms may be used interchangeably unlesscontext clearly indicates otherwise. The processor may be part of aserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or include a signal processor, digital processor,embedded processor, microprocessor or any variant such as a co-processor(math co-processor, graphic co-processor, or communication co-processor)and the like that may directly or indirectly facilitate execution ofprogram code or program instructions stored thereon. In addition, theprocessor may enable execution of multiple programs, threads, and codes.The threads may be executed simultaneously to enhance the performance ofthe processor and to facilitate simultaneous operations of theapplication. By way of implementation, methods, program codes, programinstructions and the like described herein may be implemented in one ormore thread. The thread may spawn other threads that may have assignedpriorities associated with them; the processor may execute these threadsbased on priority or any other order based on instructions provided inthe program code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,Internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The methods,programs or codes as described herein and elsewhere may be executed bythe server. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope. Inaddition, any of the devices attached to the server through an interfacemay include at least one storage medium capable of storing methods,programs, code and/or instructions. A central repository may provideprogram instructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, Internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope. Inaddition, any of the devices attached to the client through an interfacemay include at least one storage medium capable of storing methods,programs, applications, code and/or instructions. A central repositorymay provide program instructions to be executed on different devices. Inthis implementation, the remote repository may act as a storage mediumfor program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, 4G, LTE, EVDO, mesh, or other networks types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it may beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general-purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It may further be appreciated that one or more of the processesmay be realized as a computer executable code capable of being executedon a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the methods and systems described herein have been disclosed inconnection with certain preferred embodiments shown and described indetail, various modifications and improvements thereon may becomereadily apparent to those skilled in the art. Accordingly, the spiritand scope of the methods and systems described herein is not to belimited by the foregoing examples, but is to be understood in thebroadest sense allowable by law.

Additional details of exemplary and non-limiting embodiments arerecounted in Appendix A attached hereto and filed herewith.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A method of electronic communication assistance,the method comprising: intercepting, by an artificial intelligenceassistant computing facility, an electronic communication forming partof a dialog between a first user and a second user and transmitted fromthe first user and directed to the second user, the interceptingoccurring prior to further transmission of the electronic communicationto the second user, wherein the electronic communication was transmittedusing a first electronic identifier associated with the first user and asecond electronic identifier associated with the second user, theelectronic communication comprising a communication dialog content;retrieving from a communication profile database a communication profilefor the second user using the second electronic identifier, wherein thecommunication profile comprises one or more second user lexical writingcomprising: a percent of characters per document; a mean number of wordsper sentence; and a difference between a maximum number of words persentence and a minimum number of words per sentence; processing theelectronic communication with a processor to generate language that is acompositional change for the communication dialog content of theelectronic communication by optimizing for impact and effectiveness ofthe language using the one or more second user lexical writing features;and generating a changed electronic communication from the electroniccommunication and the compositional change, wherein the changedelectronic communication comprises annotations to indicate thecompositional change to the electronic communication.
 2. The method ofclaim 1, further comprising transmitting the changed electroniccommunication to the first user using the first electronic identifierassociated with the first user.
 3. The method of claim 1, furthercomprising transmitting the changed electronic communication using thesecond electronic identifier associated with the second user.
 4. Themethod of claim 1, wherein the compositional change is derived in partfrom representations of previous content and context from a plurality ofuser profiles stored in the communication profile database which aresimilar to the communication profile of the second user.
 5. The methodof claim 1, wherein the processor uses at least one of a machinelearning language model or a statistical algorithm for creating thecompositional change.
 6. The method of claim 1, wherein the processorgenerates the compositional change in part by replicating acommunication style of the first user as determined by the processorusing one or more first user lexical writing features.
 7. The method ofclaim 1, wherein the electronic communication further comprises acommunication goal, and the processor generates the language that is thecompositional change by optimizing for impact and effectiveness of thelanguage with respect to the communication goal.
 8. The method of claim1, wherein the one or more second user lexical writing features furthercomprises: a standard deviation of words per sentence.
 9. A method ofelectronic communication assistance, the method comprising: interceptingan electronic communication from further transmission at an artificialintelligence assistant computing facility, wherein the electroniccommunication forms part of a dialog between a first user and a seconduser and was transmitted from the first user and directed to the seconduser using a first electronic identifier associated with the first userand a second electronic identifier associated with the second user, theelectronic communication comprising a communication dialog content;retrieving a communication prone for the first user using the firstelectronic identifier, wherein the communication profile comprises oneor more first user lexical writing features comprising: a percent ofcharacters per document; a mean number of words per sentence; and adifference between a maximum number of words per sentence and a minimumnumber of words per sentence; processing the electronic communicationwith a processor to generate language that is a compositional change forthe communication dialog content of the electronic communication byoptimizing for impact and effectiveness using the one or more first userlexical writing features; and generating a changed electroniccommunication from the electronic communication and the compositionalchange, wherein the changed electronic communication comprisesannotations to indicate the compositional change to the electroniccommunication.
 10. The method of claim 9, further comprisingtransmitting the changed electronic communication to the second userusing the second electronic identifier associated with the second user.11. The method of claim 9, further comprising transmitting the changedelectronic communication to the first user using the first electronicidentifier associated with the first user.
 12. The method of claim 9,wherein the compositional change is derived from representations ofprevious content and context from a plurality of user profiles stored ina communication profile database which are similar to the communicationprofile of the first user.
 13. The method of claim 9, wherein theprocessor uses at least one of a machine learning language model or astatistical algorithm for creating the compositional change.
 14. Themethod of claim 9, wherein the processor generates the compositionalchange by replicating a communication style of the first user asdetermined by the processor from the one or more first user lexicalwriting features communication attribute.
 15. The method of claim 9,wherein the electronic communication further comprises a communicationgoal, and the processor generates the language that is the compositionalchange by optimizing for impact and effectiveness of the language withrespect to the communication goal.
 16. A system comprising: a servercomputer comprising a processor and a computer-readable storage devicethat stores instructions that, when executed by the processor, cause theprocessor to perform operations comprising: intercepting an electroniccommunication from further transmission at an artificial intelligenceassistant computing facility, wherein the electronic communication formspart of a dialog between a first user and a second user and wastransmitted from the first user and directed to the second user using afirst electronic identifier associated with the first user and a secondelectronic identifier associated with the second user, the electroniccommunication comprising a communication dialog content; extracting acommunication context from the electronic communication; encoding theelectronic communication for processing creating an encoded electroniccommunication; retrieving from a communication profile database a firstcommunication profile for the first user using the first electronicidentifier, wherein the first communication profile comprises one ormore first user lexical writing features; retrieving from thecommunication profile database a second communication profile for thesecond user using the second electronic identifier, wherein the secondcommunication profile comprises one or more second user lexical writingfeatures; processing the encoded electronic communication with theprocessor to generate language that is a compositional change for thecommunication dialog content of the electronic communication byoptimizing for impact and effectiveness of the language using at leastone of the communication context and at least one of the one or morefirst user lexical writing features, or the one or more second userlexical writing features, wherein the at least one of the one or morefirst user lexical writing features or the one or more second userlexical writing features used to generate the language comprises: apercent of characters per document; a mean number of words per sentence;and a difference between a maximum number of words per sentence and aminimum number of words per sentence; and generating a changedelectronic communication from the electronic communication and thecompositional change, wherein the changed electronic communicationcomprises annotations to indicate the compositional change to theelectronic communication.
 17. The system of claim 16, wherein theprocessor generates the compositional change by replicating acommunication style of the first user as determined by the processorfrom the one or more first user lexical writing features communicationattribute.
 18. The system of claim 16, wherein the electroniccommunication further comprises a communication goal, and the processorgenerates the language that is the compositional change by optimizingfor impact and effectiveness of the language with respect to thecommunication goal.
 19. The system of claim 16, wherein the processoruses at least one of a machine learning language model or a statisticalalgorithm for creating the compositional change.